Prediction of comorbid anxiety and depression using machine learning models in cancer survivors

Prediction of comorbid anxiety and depression using machine learning models in cancer survivors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Prediction of comorbid anxiety and depression using machine learning models in cancer survivors Rui Yan, Jiwei Wang, Xiaoguang Yang, Jinming Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-32449/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background We aimed to investigate the prevalence and associated factors of comorbid anxiety and depression (CAD) in Chinese cancer survivors (CS), and develop novel prediction model using traditional logistic regression and machine-learning algorithms to predict CS’ CAD, which would be helpful to accurately distinguish individuals with high risk of psychological problems. Methods A cross-sectional study of 1546 CSs were conducted in Shanghai China. Self-reported structured questionnaire was used to collect information about basic socio-demographic factors, socioeconomic status, life behavior, health conditions, fatigue, social support, anxiety (Zung self-rating anxiety scale) and depression (Zung self - rating depression scale). Stepwise logistic regression and three machine learning algorithms (support vector machine, decision tree, and random forest) were used to construct the prediction model of CS’ CAD. Results 18.24% CSs reported CAD. The AUC of models to predict the high-risk CAD in the training set was best in that of RF (0.839), followed by LR (0.811), SVM (0.808), and DT (0.794). Social support and several comorbid chronic diseases were important top ranking contributing factors of CAD in all the predictive models. Conclusion CAD was a common psychological problems in cancer survivors (CSs).The machine leaning techniques and logistic regression showed similar performance to classify Chinese CSs at risk of CAD using questionnaire-based survey. Effective treatment to control the coexisting disease, more social support should be provided to CSs to improve psychological health. Psychological care was recommend to be incorporated into regular health care and management of cancer survivors. Medical Informatics anxiety depression cancer survivor machine learning prediction model Introduction Cancer has become one of the major public health problems threatening individuals’ health, and it is estimated that one in five global cancer patients live in China( 1 ). An individual is considered as a cancer survivor (CS) since cancer diagnosis, throughout the balance of his or her life ( 2 ). Although the better primary health care and continuing medical advances allowed CS could live longer after cancer diagnosis ( 1 ), CS still face a range of challenges from physical, social, professional and economic factors. These challenges may also case psychological issues that might be long-term existed problems throughout CS’ entire survival( 3 ). Anxiety and depression are common psychological problems in CSs( 4 ). Anxiety and depression often co-occur in clinical practice( 5 ). Among individuals with depression, 67% also had a current comorbid anxiety. And among persons with anxiety, 63% had a current comorbid depressive disorder( 5 ). Anxiety or depression was associated with the development and progression of cancer. An individual is considered as a comorbid anxiety and depression (CAD) when she/he has both current anxiety and depression disorder. The contribution of CAD on functional impairment and cancer progression may substantially exceeded the contribution of their independent parts( 6 , 7 ). Since the presence of CAD represent one of great psychological burden and distress in CSs, identifying available risk factors of CAD and building prediction models more accurately may be beneficial to early prevention strategies for CSs with high risk of CAD in the most high-risk population. Screening for anxiety and depression is resource-intensive and depends on access to medical resources. Walker et al. found that 73 percent of cancer patients with depression did not receive effective treatment, and only 5 percent of cancer patients saw a psychologist( 8 ). However, in China, the supply of psychiatrists and psychologist is insufficient, and physician have little time to actively consult patients about their emotional and psychological problems. Physicians were insensitive to patients' emotions and non-verbal information, screening psychological distress in cancer patients through anxiety and depression scale was often regarded as a burden for physicians. So, building an accurate CAD model would help the physicians based on simple indicators to identify those with high-risk of CAD, and those with high CAD risk would be given appropriate advice and transfer treatment. In recent years, machine learning algorithms had been widely used to predict outcomes and guide decisions in clinical practice. Several studies evaluate individuals’ mental health by using common machine learning techniques, such as support vector machine (SVM) and decision trees (DT) and random forest (RF) ( 9-11 ), which shown that machine learning algorithms improved the accuracy of prediction. Furthermore, the recent advancement of machine learning puts more importance in explaining/interpreting machine learning trying to open the “black box” of machine learning algorithms( 12 ), particularly probe into the model-based variable importance evaluation to visualize the effect of features on outcome. However, few application of machine learning was found to build prediction models and to classify cancer survivors with high-risk of CAD. The purpose of this study was to examine the prevalence of CAD basing on a cross-sectional study. We also investigated the associated factors with CAD, and establish prediction models for CAD in Chinese CSs using traditional logistic regression (LR) and several machine learning algorithms, including random forest (RF), decision trees (DT), and support vector machines (SVM). Additionally, by comparing the four predictive models, we also aimed to find several top contributing variables that more associated with the CAD, which may be helpful to develop reference tools assisting doctors to predict mental disorders and support patient care. Methods Study population Data were acquired from a cross-sectional study of 1546 CSs, in Shanghai Cancer Rehabilitation Club (SCRC), from June to September 2018. All the recruited participants were new members registered to SCRC in 2018, pathologic diagnosed with cancer, able to independently participate in the activities of SCRC. Participants were asked to finish a self-reported structured questionnaire including a range of questions about basic socio-demographic factors, socioeconomic status, life behavior, health conditions, social support, anxiety and depression. Informed consent was obtained from each study participant. Our study was approved by the Medical Research Ethics Committee of the school of public health, Fudan University (The international registry NO. IRB00002408 &FWA00002399). Target variable: Comorbid anxiety and depression (CAD) As a dependent variable, CAD was considered as CSs who were both anxiety and depression. Anxiety and Depression was assessed by using the Zung self-rating anxiety scale (SAS)( 13 ) and Zung self-rating depression scale (SDS)( 14 ), respectively. Both SAS and SDS were a 20-item self-administrated scale, and each question is scored on a scale of 1 to 4 (rarely, sometimes, frequently, and always). The total score of each scale ranges between 20 and 80, and were then multiplied by 1.25 to obtain a standard scale. SAS standard scores ≥50 indicated anxiety and SDS standard scores ≥53 indicated depression. The respondents who experienced both anxiety and depression were categorized within the CAD group. Contributing features Contributing features included basic socio-demographic factors (age, gender and marital status), socioeconomic status (education level, working status and income), life behavior (smoking, drinking, dietary intake frequency of vegetables, fruits, fish, shrimp/crab/shell, eggs, milk, bean products and nuts), health conditions (BMI, comorbid chronic disease, cancer treatment, time since cancer diagnosis, recurrence and metastasis), and social support. Marital status was divided as married and divorced/widowed/ separated/single. Education level was categorized as less than senior high school, senior high school and above senior high school. Income was categorized as <2000 yuan/month, 2000-4000 yuan/month and ≥4000 yuan/month. BMI was categorized as <18.5 kg/m 2 , 18.5–22.9 kg/m 2 , 23.0–27.4 kg/m 2 and ≥27.5 kg/m 2 according to the World Health Organization (WHO) recommendation for Asians. Surgery, radiotherapy, chemotherapy, traditional Chinese medicine, biotherapy, recurrence and metastasis were divided as yes and no. Time since cancer diagnosis was categorized as <1 years, 1~3 years, 3~5 years and ≥5 years. Questionnaire included questions about a list of comorbid chronic diseases (CCD), including hypertension, hyperlipidemia, hyperuricemia, diabetes mellitus, heart and cardiovascular diseases, stroke, respiratory diseases, digestive diseases, and musculoskeletal diseases. And each type of CCD was categorized as “yes” or “no”. All these CCD must be clinical diagnosed by physician from secondary or tertiary hospitals in China. Smoking was categorized as never smoked, former smoker and current smoker. Drinking frequency was categorized as no, occasionally and usually. Dietary intake frequency of each food items (vegetables, fruits, eggs, fish and nuts) were obtained through a food frequency questionnaire (FFQ) that included 4 frequency categories for each kind of food (<1 times/week, 1-2 times/week, 3-4 times/week and ≥5 times/week). The level of physical activity was measured by the long form of the International Physical Activity Questionnaire (IPAQ)( 15 ) , and according to the IPAQ scoring guideline, physical activity level were then categorized into three groups: high, moderate, and low. Social Support was assessed by the Multidimensional Scale of Perceived Social Support (MSPSS) ( 16 ). MSPSS is composed of 12 items to measure perceived social support form family, friends and a significant other. Respondents use a 7-point Likert-type scale (from “very strongly disagree” to “very strongly agree”) with each item. The total MSPSS score was calculated by adding all the item scores together and then dividing by 12, and higher total scores represent higher social support. Machine learning algorithms to predict CAD Three machine learning algorithms were used to train models to predict CAD: Support Vector Machine (SVM)( 17 ), Decision Tree (DT)( 18 ) and Random Forest (RF)( 19 ). The data set (n=1546) was randomly divided as a training set (n=1160, 75%) to train prediction models and a testing set (n=386, 25%) to evaluate the real performance of the prediction methods. Since using models with feature selection was more efficient than that searching routine for all external predictors into the model, we applied the feature selection by filter to the entire training set with cross-validate by the R package caret . And 13 features (gender, cancer site, hypertension, hyperlipidemia, heart disease, stroke, respiratory diseases, digestive diseases, musculoskeletal diseases, smoking, fish intake frequency, egg intake frequency, and social support) were finally selected using simple univariate statistical methods and was then used to build machine learning models. A 10-fold cross validation was implemented to tune hyper-parameters and to prevent performance overfitting. This means that, the training dataset was split in 10 equally-sized random folds, at each time, a random subsample containing 90% of the training data was used to train a prediction model, and the remaining 10% part of the training data was used as validation. The above process was repeated 10 times until all folds had served as the test set. Via the 10-fold cross validation, the optimal hyper-parameters were searched through grid search for each machine learning prediction methods. The area under the receiver operating characteristics (ROC) curve (AUC) was used to assess performance during parameter selection. The optimal parameters of each machine learning algorithms selected by grid search were：SVM (cost=3, gamma=0.005); DT (maxdepth=5, minbucket=5, cp=0.005, xval=5); RF (maxnodes=13; mtry=6, ntree=500). These final optimal hyper-parameters were then passed to the machine learning models and applied to the testing set to evaluate the performance of the prediction methods on new data. Sensitivity, specificity, accuracy and AUC were used as evaluation measures to predict CSs with CAD. For the machine learning models with optimal hyper-parameters, model-based variable importance evaluation was conducted to quantifying the importance of each feature. Statistical Analysis Means and standard deviations were calculated for continuous variables, and numbers and percentages were computed for categorical variables. The distribution of CAD among different socio-demographic factors, socioeconomic status, life behavior, health conditions were compared using Chi-square test or Students’t-test. Multivariate logistic regression was used to identify factors associated with CAD using the odds ratio (OR) and its corresponding 95% confidence interval (95%CI), adjusted for all other confounders. A stepwise logistic regression( 20 ) was used to build the final model for predicting CAD. The machine learning algorithm was developed using R 3.6.1 with the package mlr 2.15. All statistical analyses were performed by R version 3.6.1. A two-sided P value < 0.05 was considered significant. Results In our study, the top five cancer site were breast cancer (n=491), lung cancer (n=241), colorectal cancer (n=179), thyroid cancer (n=123) and gastric cancer (n=120). Combining all cancer sites, the prevalence of CAD in CS was 18.24%, and the prevalence of CAD (9.76%‐25.71%) varied by cancer site. (Table 1). The characteristics for a total of 1546 CSs are summarized in Table 2. After adjusted for potential confounding variables, when compared with male CSs, female CSs had higher risk of CAD (OR = 2.12, 95% CI: 1.51-2.97) (Table 2). 50 to 59 years old CSs had higher levels of CAD than those below 50 years of age (OR = 1.80, 95% CI: 1.06-3.07). CSs with underweight had higher risk of CAD than those with normal weight (OR =1.81, 95% CI: 1.01-3.23). CSs underwent chemotherapy had higher risk of CAD than those without chemotherapy. CS with diagnoses of hyperlipidemia(OR = 1.49, 95% CI: 1.13‐1.96), Diabetes(OR = 1.47, 95% CI: 1.06‐2.05), heart and cardiovascular diseases(OR = 1.93, 95% CI: 1.40‐2.66), stroke(OR = 2.07, 95% CI: 1.20‐3.56), respiratory diseases (OR =2.29, 95% CI: 1.61‐3.27), Digestive diseases (OR =1.38, 95% CI: 1.07‐1.79) and musculoskeletal diseases (OR = 2.41, 95% CI: 1.79‐3.24) were significantly more likely to report CAD. CS with frequent intake of fish, Shrimp/crab/shell, milk and nuts ≥5 times/week had lower risk of CAD than those with intake of these less than 1 times/week. Decreased risk of CAD was found among those with higher scores of social support (OR =0.97, 95% CI: 0.96-0.99). Transitional logistic regression and three machine learning methods were used to predict CAD for CS. The predictive performance ROC plots of training set and testing set were shown in Table 3. In the training set, the ROC curve shown that the diagnostic performance of the four logistic regression and machine learning algorithms to predict CAD ranked from high to low were RF, LR, SVM, and DT. In the testing set, RF classifier achieved the best prediction performance with 0.771, 0.701, 0.68 and 0.684 in AUC, accuracy (Acc.), and sensitivity (Sens.) and specificity (Spec.). Similar prediction performance was found in logistic regression with a sensitivity of 0.675, specificity of 0.693, classification accuracy of 0.689, and AUC of 0.769. Feature importance Table 4 shown the feature importance ranking for predicting CAD in logistic regression and the three machine learning models. Seven of the thirteen contributing factors (gender, fish intake frequency, hypertension, digestive diseases, musculoskeletal diseases, stroke and social support) were identified in both logistic regression and machine learning models. Additionally, the RF model also put emphasis on age, marital status, working status, time since diagnosis and radiotherapy. Discussion This study examined the association of comorbid anxiety and depression with various factors among and used different machine learning algorithms to predict CAD among 1546 Chinese cancer survivors. Our study indicated that the prevalence of CAD was 18.2%, and random forest (RF) and logistic regression achieved similar predictive performance with 0.771 and 0.769 in AUC in testing set, respectively. Social support and comorbid chronic diseases were important top ranking associated factors with CAD in all the predictive models. Our results indicated that nearly one in five cancer survivors reported CAD which indicated that CAD had become a severe psychological distress and burden that faced by numerous CSs. As two negative psychological problems, anxiety and depression may decline body's immunity, and be associated with the development and progression of cancer. Anxiety and depression might also lead to lower adherence to treatment, higher suicide risk, and additional health expenditures ( 21 , 22 ). Compared with CS with simple anxiety or depression, CAD was associated with more complex symptom, and greater negative impact on the cancer treatment effect and CS’ quality of life. It is important to found risk factors of CAD, reduce the prevalence of CAD, conduct psychological health promotion, and finally improve CS’ psychological health. Machine learning algorithms have several advantages beyond traditional statistical approaches in building predictive model ( 23 ): no linear requirement for data distribution, discovery and utilization of the complex interaction and non-linear relationships among related factors automatically, and high tolerance to outliers and missing values. These advantages allowed machine learning techniques has high application value in predictive classification. In this study, we applied the several machine learning algorithms to develop predictive models to determine CSs at risk of CAD. Among the three machine learning algorithms, in external validation, RF shown the best prediction performance in the testing dataset with an accuracy of 68.4% (AUC = 0.771, 95%CI=0.714, 0.829). However, the performance of RF was similar to the performance of logistic regression (AUC = 0.769, 95%CI=0.711, 0.862). Our study indicated that although machine learning algorithms have the potential to identify cancer survivors at risk of CAD using questionnaire-based survey, however the difference in AUC between LR and random forest was not statistically significant. A previous systematic review indicated that clinical prediction models based on machine learning would not lead to better AUC than that based on LR( 24 ). Logistic regression is a powerful tool that easy to understand and implement, especially in epidemiologic studies. Future studies would possibly show a better performance if a single machine learning algorithm is combined with various algorithms rather than a single algorithm model. Machine learning were considered as “black-box”, and had limitations to reveal how features are interacting and what effect they have on the outcome ( 15 , 25 ). However, the feature importance provided information evaluate the importance ranking of each factor in the classification decision. Six of thirteen selected features were comorbid chronic diseases. Our previous study had shown that comorbid chronic diseases was associated with greater probability of anxiety and depression( 26 ), which indicating that physical problem might had great influence on CSs’ psychological health, and effectively monitoring and management of CCDs should be provided for CSs to address the psychological and psychical problems. Social support was another important predictor of CAD. Social support refers to the material and spiritual support that provided from family, friends and others. The association between social support and anxiety, depression among cancer patients had been proven in previous studies ( 27 , 28 ). Cancer may had negative influence on individuals’ social relationships and the support the patient receives after cancer diagnosis. Social support may act as a coping resource that could deal with the side effect of cancer treatments and mitigate the adverse psychological issues. Several limitations of our study should be acknowledged. First, due to the good reliability and validity of SAS and SDS in Chinese population ( 29 , 30 ), we used questionnaire-based scales to screen anxiety and depression rather than a clinical diagnosis of either. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM ‐5) should be as gold standard diagnostic instruments in further studies. Second, several important clinical indicators that may associated with psychological health, such as cancer stage and pathological type, were not collected in our study. The performance of our predictive model might be improved if we composed of more detailed basic questions. Third, larger sample size of CSs from multicenter should be included in the future study to verify and improve the stability and reliability of the current machine learning models. At last, the relationship between CAD and several factors, such as physical activity and comorbid chronic diseases, might be bidirectional. Given the cross-sectional nature of current study, we cannot deduce the causal relationships between associated factors and CAD. Several clinical implication of our study should also be emphasized. A high percentage of Chinese CSs suffered from both anxiety and depression, and it could be beneficial to screen these psychological distress and improve CS’ quality of life. However, in China, the cancer management is more focused on cancer treatment outcome, instead of psychological health. Psychological problems was infrequently discussed or treated, and remain on the periphery of cancer care. Both logistic regression and machine learning models indicated several intervention targets to improve CSs’ psychological health, such as frequently intake of fish and egg, and effective treatment to control the coexisting disease, and provide more social support. Psychological care was strongly recommend to be incorporated into regular health care and management of cancer survivors. Additionally, the cooperation of oncologist, physician , psychologist, family and others were needed to provide supportive collaborative care. In conclusion, our study shown that machine learning techniques have the potential to identify Chinese cancer survivors at risk of CAD using questionnaire-based survey of socio-demographic factors, socioeconomic status, life behaviors, health conditions and social support. Random forest model and logistic regression have the similarly good model performance, and would facilitate early screening of high risk of CAD and changing modifiable risk factors. Declarations Acknowledgements: Shanghai Cancer Rehabilitation Club provided invaluable resources for field investigation. We are grateful to all involved cancer survivors to participate in this study. We also thank all workers and volunteers involved in the acquisition of data. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Conflict of Interest: The authors declare that they have no conflict of interest. Research involving human participants ： The study was approved by the Medical Research Ethics Committee of the school of public health, Fudan University (The international registry NO. IRB00002408 &FWA00002399). Informed consent: Informed consent was obtained from all individual participants included in the study. Data availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Authors ’ contributions Rui Yan and Xiaoguang Yang proposed and designed the study. Rui Yan and Xiaoguang Yang built models and wrote the paper. Rui Yan and Xiaoguang Yang wrote the paper. Rui Yan and Jiwei Wang did ground work of collecting original data and revised the paper. Jinming Yu supervised the study and revised the paper. All authors have read and approved the final manuscript. Reference Zeng H, Chen W, Zheng R, Zhang S, Ji JS, Zou X, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. The Lancet Global health. 2018;6(5):e555-e67. Denlinger CS, Carlson RW, Are M, Baker KS, Davis E, Edge SB, et al. Survivorship: introduction and definition. 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Tables Table 1 Prevalence of anxiety and depression among CSs by tumor site Cancer Site Number CAD N % All sites 1546 282 18.24% Breast 491 103 20.98% Lung 241 34 14.41% Colorectum 179 32 17.88% Thyroid 123 28 22.76% Stomach 120 19 15.83% Cervix 45 9 20.00% Liver 41 4 9.76% Uterus 35 9 25.71% Ovary 34 8 23.53% Testis 30 5 16.67% All others a 207 31 14.98% Abbreviations: CAD, comorbid anxiety and depression a All other cancer sites include kidney (N = 28), nasopharynx (N = 22), prostate (N = 21), bladder (N = 20), oral cavity (N = 12), esophagus (N = 10), pancreas (N = 10), gallbladder (N = 8), leukemia (N = 8), melanoma of skin (N = 7), larynx (N = 6), bone (N = 4), brain (N = 2), and other cancer sites (N = 28). Table 2 Prevalence of CAD in CS among various basic characters Characteristics N(%) CAD  2 /t P OR(95%CI) a No N(%) Yes N(%) Gender Male 415(26.84) 369(88.92) 46(11.08) ref Female 1131(73.16) 895(79.13) 236(20.87) 19.48 <.001 2.12(1.51,2.97) Age (years) <50 130(8.41) 112(86.15) 18(13.85) ref 50-59 584(37.77) 453(77.57) 131(22.43) 1.80(1.06,3.07) 60-69 733(47.41) 611(83.36) 122(16.64) 1.24(0.73,2.12) ≥ 70 99(6.40) 88(88.89) 11(11.11) 13.19 0.004 0.78(0.35,1.73) Marital status Married 1374(88.87) 1115(81.15) 259(18.85) ref Unmarried/widowed/divorced 172(11.13) 149(86.63) 23(13.37) 3.08 0.080 0.67(0.42,1.05) Education senior high school 177(11.45) 153(86.44) 24(13.56) 3.02 0.221 0.67(0.42,1.06) Household per capita income (yuan/month) <2000 278(17.98) 221(79.50) 57(20.50) ref 2000~ 677(43.79) 561(82.87) 116(17.13) 0.80(0.56,1.14) ≥ 4000 591(38.23) 482(81.56) 109(18.44) 1.53 0.466 0.88(0.61,1.25) Working status On-the-job 58(3.75) 50(86.21) 8(13.79) ref Sick leave 49(3.17) 41(83.67) 8(16.33) 1.22(0.42,3.53) Unemployed/retired 1439(93.08) 1173(81.51) 266(18.49) 0.95 0.623 1.42(0.66,3.02) Living alone No 1468(94.95) 1199(81.68) 269(18.32) ref Yes 78(5.05) 65(83.33) 13(16.67) 0.14 0.712 0.89(0.48,1.64) BMI (kg/m 2 ) Underweight (<18.5) 63(4.08) 46(73.02) 17(26.98) 1.81(1.01,3.23) Normal weight (18.5–24.9 ) 1020(65.98) 847(83.04) 173(16.96) ref Pre-obese (25.0–29.9) 401(25.94) 322(80.30) 79(19.70) 1.20(0.89,1.61) Obese ( ≥ 30 ) 62(4.01) 49(79.03) 13(20.97) 5.23 0.156 1.30(0.69,2.45) Time since diagnosis (years) <1 86(5.56) 69(80.23) 17(19.77) ref 1~ 604(39.07) 505(83.61) 99(16.39) 0.8(0.45,1.41) 3~ 526(34.02) 415(78.9) 111(21.1) 1.09(0.61,1.92) ≥ 5 330(21.35) 275(83.33) 55(16.67) 4.96 0.175 0.81(0.44,1.49) Cancer Treatment Surgery No 139(8.99) 117(84.17) 22(15.83) ref Yes 1407(91.01) 1147(81.52) 260(18.48) 0.60 0.440 1.21(0.75,1.94) Radiotherapy No 1206(78.01) 990(82.09) 216(17.91) ref Yes 340(21.99) 274(80.59) 66(19.41) 0.40 0.527 1.1(0.81,1.5) Chemotherapy No 549(35.51) 467(85.06) 82(14.94) ref Yes 997(64.49) 797(79.94) 200(20.06) 6.23 0.013 1.43(1.08,1.89) TMC No 686(44.37) 565(82.36) 121(17.64) ref Yes 860(55.63) 699(81.28) 161(18.72) 0.20 0.657 1.08(0.83,1.4) Biotherapy No 1453(93.98) 1192(82.04) 261(17.96) ref Yes 93(6.02) 72(77.42) 21(22.58) 1.25 0.264 1.33(0.81,2.21) Recurrance No 1454(94.05) 1191(81.91) 263(18.09) ref Yes 92(5.95) 73(79.35) 19(20.65) 0.38 0.573 1.18(0.7,1.99) Metastasis No 1463(94.63) 1200(82.02) 263(17.98) ref Yes 83(5.37) 64(77.11) 19(22.89) 1.27 0.259 1.36(0.8,2.3) Comorbid chronic diseases Hypertension No 1028(66.49) 846(82.3) 182(17.7) ref Yes 518(33.51) 418(80.69) 100(19.31) 0.59 0.442 1.11(0.85,1.46) Hyperlipidemia No 1110(71.8) 927(83.51) 183(16.49) ref Yes 436(28.2) 337(77.29) 99(22.71) 8.12 0.004 1.49(1.13,1.96) Hypeluricemia No 1451(93.86) 1194(82.29) 257(17.71) ref Yes 95(6.14) 70(73.68) 25(26.32) 4.43 0.03 1.66(1.03,2.67) Diabetes No 1308(84.61) 1082(82.72) 226(17.28) ref Yes 238(15.39) 182(76.47) 56(23.53) 5.28 0.022 1.47(1.06,2.05) Heart and cardiovascular No 1311(84.8) 1094(83.45) 217(16.55) ref Yes 235(15.2) 170(72.34) 65(27.66) 16.49 0 .001 1.93(1.40,2.66) Stroke No 1481(95.8) 1219(82.31) 262(17.69) ref Yes 65(4.2) 45(69.23) 20(30.77) 7.14 0.008 2.07(1.20,3.56) Respiratory diseases No 1377(89.07) 1148(83.37) 229(16.63) ref Yes 169(10.93) 116(68.64) 53(31.36) 21.90 <0.001 2.29(1.61,3.27) Digestive diseases No 793(51.29) 667(84.11) 126(15.89) ref Yes 753(48.71) 597(79.28) 156(20.72) 6.04 0.014 1.38(1.07,1.79) Musculoskeletal diseases No 1269(82.08) 1072(84.48) 197(15.52) ref Yes 277(17.92) 192(69.31) 85(30.69) 35.05 <0.001 2.41(1.79,3.24) Number of comorbidity 0 388(25.1) 335(86.34) 53(13.66) ref 1-2 694(44.89) 588(84.73) 106(15.27) 1.14(0.8,1.63) ≥ 3 464(30.01) 341(73.49) 123(26.51) 30.82 <0.001 2.28(1.60,3.25) Smoking Never 1259(81.44) 1010(80.22) 249(19.78) ref Ever 229(14.81) 200(87.34) 29(12.66) 0.59(0.39,0.89) Current 58(3.75) 54(93.1) 4(6.9) 11.77 0.003 0.3(0.11,0.84) Drinking Never 1177(76.13) 958(81.39) 219(18.61) ref Ever 333(21.54) 273(81.98) 60(18.02) 0.96(0.7,1.32) Current 36(2.33) 33(91.67) 3(8.33) 2.49 0.289 0.4(0.12,1.31) Physical activity Low 240(15.52) 190(79.17) 50(20.83) ref Moderate 404(26.13) 323(79.95) 81(20.05) 0.95(0.64,1.42) High 902(58.34) 751(83.26) 151(16.74) 3.33 0.189 0.76(0.54,1.09) Sedentary time 6 h/day 200(12.94) 161(80.5) 39(19.5) 3.53 0.171 1.22(0.82,1.8) Food frequency Vegetables <1 times/week 44(2.85) 31(70.45) 13(29.55) ref 1-2 times/week 50(3.23) 42(84) 8(16) 0.45(0.17,1.23) 3-4 times/week 91(5.89) 75(82.42) 16(17.58) 0.51(0.22,1.18) ≥5 times/week 1361(88.03) 1116(82) 245(18) 4.02 0.260 0.52(0.27,1.02) Fruits <1 times/week 80(5.17) 59(73.75) 21(26.25) ref 1-2 times/week 126(8.15) 98(77.78) 28(22.22) 0.8(0.42,1.54) 3-4 times/week 195(12.61) 170(87.18) 25(12.82) 0.41(0.22,0.79) ≥5 times/week 1145(74.06) 937(81.83) 208(18.17) 8.63 0.035 0.62(0.37,1.05) Eggs <1 times/week 110(7.12) 84(76.36) 26(23.64) ref 1-2 times/week 218(14.1) 176(80.73) 42(19.27) 0.77(0.44,1.34) 3-4 times/week 316(20.44) 259(81.96) 57(18.04) 0.71(0.42,1.20) ≥5 times/week 902(58.34) 745(82.59) 157(17.41) 2.73 0.435 0.68(0.42,1.09) Fish <1 times/week 178(11.51) 134(75.28) 44(24.72) ref 1-2 times/week 549(35.51) 455(82.88) 94(17.12) 0.63(0.42,0.95) 3-4 times/week 447(28.91) 368(82.33) 79(17.67) 0.65(0.43,0.99) ≥5 times/week 372(24.06) 307(82.53) 65(17.47) 5.71 0.126 0.65(0.42,0.99) Shrimp/crab/shell <1 times/week 568(36.74) 438(77.11) 130(22.89) ref 1-2 times/week 574(37.13) 492(85.71) 82(14.29) 0.56(0.41,0.76) 3-4 times/week 230(14.88) 187(81.3) 43(18.7) 0.78(0.53,1.14) ≥5 times/week 174(11.25) 147(84.48) 27(15.52) 15.14 0.002 0.62(0.39,0.98) Nuts <1 times/week 604(39.07) 472(78.15) 132(21.85) ref 1-2 times/week 385(24.9) 320(83.12) 65(16.88) 0.73(0.52,1.01) 3-4 times/week 226(14.62) 183(80.97) 43(19.03) 0.84(0.57,1.23) ≥5 times/week 331(21.41) 289(87.31) 42(12.69) 12.70 0.005 0.52(0.36,0.76) Milk <1 times/week 299(19.34) 231(77.26) 68(22.74) ref 1-2 times/week 213(13.78) 178(83.57) 35(16.43) 0.67(0.43,1.05) 3-4 times/week 227(14.68) 186(81.94) 41(18.06) 0.75(0.49,1.15) ≥5 times/week 807(52.2) 669(82.9) 138(17.1) 5.24 0.155 0.70(0.51,0.97) Bean <1 times/week 320(20.7) 257(80.31) 63(19.69) ref 1-2 times/week 518(33.51) 430(83.01) 88(16.99) 0.84(0.58,1.20) 3-4 times/week 396(25.61) 323(81.57) 73(18.43) 0.92(0.63,1.34) ≥5 times/week 312(20.18) 254(81.41) 58(18.59) 1.03 0.794 0.93(0.63,1.39) Social support score 66.18±10.20 62.64±11.35 <.0001 0.97(0.96,0.99) Abbreviations: CAD, comorbid anxie ty and depression; BMI, body mass index; TMC, traditional Chinese medicine a adjusted for all other factors Table 3 The predictive performance of each model Training set Testing set Sens Spec Acc AUC(95%CI) Sens Spec Acc AUC (95%CI) LR 0.761 0.703 0.715 0.811(0.781-0.841) 0.675 0.693 0.689 0.769(0.711-0.826) SVM 0.709 0.765 0.753 0.808(0.777-0.838) 0.688 0.605 0.622 0.747(0.693-0.801) DT 0.725 0.746 0.741 0.794(0.760-0.827) 0.701 0.621 0.637 0.682(0.618-0.747) RF 0.789 0.735 0.747 0.839(0.812-0.869) 0.701 0.68 0.684 0.771(0.714-0.829) Abbreviations: Sens, sensitivity; Spec, specificity; Acc, accuracy; AUC, the area under the receiver operating characteristics curve; LR, logistic regression; RF, Random Forest; DT, Decision Tree; SVM, Support Vector Machine Table 4 Feature importance ranking for predicting CAD in training set Feature Importance Ranking LR RF DT SVM 1 Musculoskeletal diseases Cancer site Musculoskeletal diseases Hypertension 2 Fish intake frequency Heart disease Cancer site Hyperlipidemia 3 Gender Musculoskeletal diseases Gender Gender 4 Social Support Social Support Smoking Musculoskeletal diseases 5 Stroke Fish intake frequency Hyperlipidemia Fish intake frequency 6 Marital status Smoking Fish intake frequency Smoking 7 Age Gender Stroke Egg intake frequency 8 work Digestive diseases Respiratory diseases Cancer site 9 Time since diagnosis Stroke Social Support Heart disease 10 Fruit intake frequency Egg intake frequency Hypertension Respiratory diseases 11 Hypertension Hypertension Heart disease Stroke 12 Digestive diseases Respiratory diseases Egg intake frequency Digestive diseases 13 Radiotherapy Hyperlipidemia Digestive diseases Social Support Abbreviations: LR, logistic regression; RF, Random Forest; DT, Decision Tree; SVM, Support Vector Machine Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-32449","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":655191,"identity":"42185b77-fa57-48b5-97ff-34b3097aceba","order_by":0,"name":"Rui Yan","email":"","orcid":"https://orcid.org/0000-0002-5330-7861","institution":"Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Yan","suffix":""},{"id":655192,"identity":"55cfc525-4aa0-4f77-b870-0692f8bd468a","order_by":1,"name":"Jiwei Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiwei","middleName":"","lastName":"Wang","suffix":""},{"id":655193,"identity":"feccdb24-5fe3-4c22-bbbd-cedf856e0c60","order_by":2,"name":"Xiaoguang Yang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoguang","middleName":"","lastName":"Yang","suffix":""},{"id":655194,"identity":"f5aba263-3992-4174-bcbc-06a6bb2f0f1c","order_by":3,"name":"Jinming Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACxgYGxgcVDMxgjgSxWpgNzpCkBQjYJEjTwjztjFnFwTZreXMG5oO3eRjs8gg7bHaO2Y2DbemGOxvYkq15GJKLidJy+2PbYcYNB3jMpHkYDiQ2EKOl4GDbYfsNB/i/Ea+FAaglEWgLG7Fa0oolDpxLT95wmM3Yco5BMmEthrOTN344UGZtu+F488MbbyrsiNDSwGEAYYGjxoCQeiCQZ2B/QISyUTAKRsEoGNEAAIGhPTHxNN91AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8537-839X","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jinming","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2020-05-29 04:04:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-32449/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-32449/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13539165,"identity":"1dc0d4c0-73d0-46d1-baa3-d820c061e69d","added_by":"auto","created_at":"2021-09-17 01:42:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":629700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-32449/v1/b2fba332-1e4f-4f2c-886e-2192cf1575be.pdf"}],"financialInterests":"","formattedTitle":"Prediction of comorbid anxiety and depression using machine learning models in cancer survivors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer has become one of the major public health problems threatening individuals\u0026rsquo; health, and it is estimated that one in five global cancer patients live in China(\u003ca href=\"#_ENREF_1\"\u003e1\u003c/a\u003e). An individual is considered as a cancer survivor (CS) since cancer diagnosis, throughout the balance of his or her life (\u003ca href=\"#_ENREF_2\"\u003e2\u003c/a\u003e). Although the better primary health care and continuing medical advances allowed CS could live longer after cancer diagnosis (\u003ca href=\"#_ENREF_1\"\u003e1\u003c/a\u003e), CS still face a range of challenges from physical, social, professional and economic factors. These challenges may also case psychological issues that might be long-term existed problems throughout CS\u0026rsquo; entire survival(\u003ca href=\"#_ENREF_3\"\u003e3\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eAnxiety and depression are common psychological problems in CSs(\u003ca href=\"#_ENREF_4\"\u003e4\u003c/a\u003e). Anxiety and depression often co-occur in clinical practice(\u003ca href=\"#_ENREF_5\"\u003e5\u003c/a\u003e). Among individuals with depression, 67% also had a current comorbid anxiety. And among persons with anxiety, 63% had a current comorbid depressive disorder(\u003ca href=\"#_ENREF_5\"\u003e5\u003c/a\u003e). Anxiety or depression was associated with the development and progression of cancer. An individual is considered as a comorbid anxiety and depression (CAD) when she/he has both current anxiety and depression disorder. The contribution of CAD on functional impairment and cancer progression may substantially exceeded the contribution of their independent parts(\u003ca href=\"#_ENREF_6\"\u003e6\u003c/a\u003e, \u003ca href=\"#_ENREF_7\"\u003e7\u003c/a\u003e). Since the presence of CAD represent one of great psychological burden and distress in CSs, identifying available risk factors of CAD and building prediction models more accurately may be beneficial to early prevention strategies for CSs with high risk of CAD in the most high-risk population.\u003c/p\u003e\n\u003cp\u003eScreening for anxiety and depression is resource-intensive and depends on access to medical resources. Walker et al. found that 73 percent of cancer patients with depression did not receive effective treatment, and only 5 percent of cancer patients saw a psychologist(\u003ca href=\"#_ENREF_8\"\u003e8\u003c/a\u003e). However, in China, the supply of psychiatrists and psychologist is insufficient, and physician have little time to actively consult patients about their emotional and psychological problems. Physicians were insensitive to patients' emotions and non-verbal information, screening psychological distress in cancer patients through anxiety and depression scale was often regarded as a burden for physicians. So, building an accurate CAD model would help the physicians based on simple indicators to identify those with high-risk of CAD, and those with high CAD risk would be given appropriate advice and transfer treatment.\u003c/p\u003e\n\u003cp\u003eIn recent years, machine learning algorithms had been widely used to predict outcomes and guide decisions in clinical practice. Several studies evaluate individuals\u0026rsquo; mental health by using common machine learning techniques, such as support vector machine (SVM) and decision trees (DT) and random forest (RF) (\u003ca href=\"#_ENREF_9\"\u003e9-11\u003c/a\u003e), which shown that machine learning algorithms improved the accuracy of prediction. Furthermore, the recent advancement of machine learning puts more importance in explaining/interpreting machine learning trying to open the \u0026ldquo;black box\u0026rdquo; of machine learning algorithms(\u003ca href=\"#_ENREF_12\"\u003e12\u003c/a\u003e), particularly probe into the model-based variable importance evaluation to visualize the effect of features on outcome. However, few application of machine learning was found to build prediction models and to classify cancer survivors with high-risk of CAD.\u003c/p\u003e\n\u003cp\u003eThe purpose of this study was to examine the prevalence of CAD basing on a cross-sectional study. We also investigated the associated factors with CAD, and establish prediction models for CAD in Chinese CSs using traditional logistic regression (LR) and several machine learning algorithms, including random forest (RF), decision trees (DT), and support vector machines (SVM). Additionally, by comparing the four predictive models, we also aimed to find several top contributing variables that more associated with the CAD, which may be helpful to develop reference tools assisting doctors to predict mental disorders and support patient care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were acquired from a cross-sectional study of 1546 CSs, in Shanghai Cancer Rehabilitation Club (SCRC), from June to September 2018. All the recruited participants were new members registered to SCRC in 2018, pathologic diagnosed with cancer, able to independently participate in the activities of SCRC. Participants were asked to finish a self-reported structured questionnaire including a range of questions about basic socio-demographic factors, socioeconomic status, life behavior, health conditions, social support, anxiety and depression. Informed consent was obtained from each study participant. Our study was approved by the Medical Research Ethics Committee of the school of public health, Fudan University (The international registry NO. IRB00002408 \u0026amp;FWA00002399).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTarget variable: Comorbid anxiety and depression\u003c/strong\u003e\u003cstrong\u003e (CAD)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs a dependent variable, CAD was considered as CSs who were both anxiety and depression. Anxiety and Depression was assessed by using the Zung self-rating anxiety scale (SAS)(\u003ca href=\"#_ENREF_13\"\u003e13\u003c/a\u003e) and Zung self-rating depression scale (SDS)(\u003ca href=\"#_ENREF_14\"\u003e14\u003c/a\u003e), respectively. Both SAS and SDS were a 20-item self-administrated scale, and each question is scored on a scale of 1 to 4 (rarely, sometimes, frequently, and always). The total score of each scale ranges between 20 and 80, and were then multiplied by 1.25 to obtain a standard scale. SAS standard scores \u0026ge;50 indicated anxiety and SDS standard scores \u0026ge;53 indicated depression. The respondents who experienced both anxiety and depression were categorized within the CAD group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributing features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContributing features included basic socio-demographic factors (age, gender and marital status), socioeconomic status (education level, working status and income), life behavior (smoking, drinking, dietary intake frequency of vegetables, fruits, fish, shrimp/crab/shell, eggs, milk, bean products and nuts), health conditions (BMI, comorbid chronic disease, cancer treatment, time since cancer diagnosis, recurrence and metastasis), and social support.\u003c/p\u003e\n\u003cp\u003eMarital status was divided as married and divorced/widowed/ separated/single. Education level was categorized as less than senior high school, senior high school and above senior high school. Income was categorized as \u0026lt;2000 yuan/month, 2000-4000 yuan/month and \u0026ge;4000 yuan/month. BMI was categorized as \u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, 18.5\u0026ndash;22.9 kg/m\u003csup\u003e2\u003c/sup\u003e, 23.0\u0026ndash;27.4 kg/m\u003csup\u003e2\u003c/sup\u003e and \u0026ge;27.5 kg/m\u003csup\u003e2\u003c/sup\u003e according to the World Health Organization (WHO) recommendation for Asians. Surgery, radiotherapy, chemotherapy, traditional Chinese medicine, biotherapy, recurrence and metastasis were divided as yes and no. Time since cancer diagnosis was categorized as \u0026lt;1 years, 1~3 years, 3~5 years and \u0026ge;5 years.\u003c/p\u003e\n\u003cp\u003eQuestionnaire included questions about a list of comorbid chronic diseases (CCD), including hypertension, hyperlipidemia, hyperuricemia, diabetes mellitus, heart and cardiovascular diseases, stroke, respiratory diseases, digestive diseases, and musculoskeletal diseases. And each type of CCD was categorized as \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo;. All these CCD must be clinical diagnosed by physician from secondary or tertiary hospitals in China.\u003c/p\u003e\n\u003cp\u003eSmoking was categorized as never smoked, former smoker and current smoker. Drinking frequency was categorized as no, occasionally and usually. Dietary intake frequency of each food items (vegetables, fruits, eggs, fish and nuts) were obtained through a food frequency questionnaire (FFQ) that included 4 frequency categories for each kind of food (\u0026lt;1 times/week, 1-2 times/week, 3-4 times/week and \u0026ge;5 times/week).\u003c/p\u003e\n\u003cp\u003eThe level of physical activity was measured by the long form of the International Physical Activity Questionnaire (IPAQ)(\u003ca href=\"#_ENREF_15\"\u003e15\u003c/a\u003e) , and according to the IPAQ scoring guideline, physical activity level were then categorized into three groups: high, moderate, and low.\u003c/p\u003e\n\u003cp\u003eSocial Support was assessed by the Multidimensional Scale of Perceived Social Support (MSPSS) (\u003ca href=\"#_ENREF_16\"\u003e16\u003c/a\u003e). MSPSS is composed of 12 items to measure perceived social support form family, friends and a significant other. Respondents use a 7-point Likert-type scale (from \u0026ldquo;very strongly disagree\u0026rdquo; to \u0026ldquo;very strongly agree\u0026rdquo;) with each item. The total MSPSS score was calculated by adding all the item scores together and then dividing by 12, and higher total scores represent higher social support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMachine learning\u003c/strong\u003e\u003cstrong\u003e algorithms to predict CAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree machine learning algorithms were used to train models to predict CAD: Support Vector Machine (SVM)(\u003ca href=\"#_ENREF_17\"\u003e17\u003c/a\u003e), Decision Tree (DT)(\u003ca href=\"#_ENREF_18\"\u003e18\u003c/a\u003e) and Random Forest (RF)(\u003ca href=\"#_ENREF_19\"\u003e19\u003c/a\u003e). The data set (n=1546) was randomly divided as a training set (n=1160, 75%) to train prediction models and a testing set (n=386, 25%) to evaluate the real performance of the prediction methods. Since using models with feature selection was more efficient than that searching routine for all external predictors into the model, we applied the feature selection by filter to the entire training set with cross-validate by the R package \u003cem\u003ecaret\u003c/em\u003e. And 13 features (gender, cancer site, hypertension, hyperlipidemia, heart disease, stroke, respiratory diseases, digestive diseases, musculoskeletal diseases, smoking, fish intake frequency, egg intake frequency, and social support) were finally selected using simple univariate statistical methods and was then used to build machine learning models.\u003c/p\u003e\n\u003cp\u003eA 10-fold cross validation was implemented to tune hyper-parameters and to prevent performance overfitting. This means that, the training dataset was split in 10 equally-sized random folds, at each time, a random subsample containing 90% of the training data was used to train a prediction model, and the remaining 10% part of the training data was used as validation. The above process was repeated 10 times until all folds had served as the test set. Via the 10-fold cross validation, the optimal hyper-parameters were searched through grid search for each machine learning prediction methods. The area under the receiver operating characteristics (ROC) curve (AUC) was used to assess performance during parameter selection. The optimal parameters of each machine learning algorithms selected by grid search were：SVM (cost=3, gamma=0.005); DT (maxdepth=5, minbucket=5, cp=0.005, xval=5); RF (maxnodes=13; mtry=6, ntree=500). These final optimal hyper-parameters were then passed to the machine learning models and applied to the testing set to evaluate the performance of the prediction methods on new data. Sensitivity, specificity, accuracy and AUC were used as evaluation measures to predict CSs with CAD. For the machine learning models with optimal hyper-parameters, model-based variable importance evaluation was conducted to quantifying the importance of each feature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeans and standard deviations were calculated for continuous variables, and numbers and percentages were computed for categorical variables. The distribution of CAD among different socio-demographic factors, socioeconomic status, life behavior, health conditions were compared using Chi-square test or Students\u0026rsquo;t-test. Multivariate logistic regression was used to identify factors associated with CAD using the odds ratio (OR) and its corresponding 95% confidence interval (95%CI), adjusted for all other confounders. A stepwise logistic regression(\u003ca href=\"#_ENREF_20\"\u003e20\u003c/a\u003e) was used to build the final model for predicting CAD. The machine learning algorithm was developed using R 3.6.1 with the package \u003cem\u003emlr\u003c/em\u003e 2.15. All statistical analyses were performed by R version 3.6.1. A two-sided P value \u0026lt; 0.05 was considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn our study, the top five cancer site were breast cancer (n=491), lung cancer (n=241), colorectal cancer (n=179), thyroid cancer (n=123) and gastric cancer (n=120). Combining all cancer sites, the prevalence of CAD in CS was 18.24%, and the prevalence of CAD (9.76%‐25.71%) varied by cancer site. (Table 1).\u003c/p\u003e\n\u003cp\u003eThe characteristics for a total of 1546 CSs are summarized in Table 2. After adjusted for potential confounding variables, when compared with male CSs, female CSs had higher risk of CAD (OR = 2.12, 95% CI: 1.51-2.97) (Table 2). 50 to 59 years old CSs had higher levels of CAD than those below 50 years of age (OR = 1.80, 95% CI: 1.06-3.07). CSs with underweight had higher risk of CAD than those with normal weight (OR =1.81, 95% CI: 1.01-3.23). CSs underwent chemotherapy had higher risk of CAD than those without chemotherapy. CS with diagnoses of hyperlipidemia(OR = 1.49, 95% CI: 1.13‐1.96), Diabetes(OR = 1.47, 95% CI: 1.06‐2.05), heart and cardiovascular diseases(OR = 1.93, 95% CI: 1.40‐2.66), stroke(OR = 2.07, 95% CI: 1.20‐3.56), respiratory diseases (OR =2.29, 95% CI: 1.61‐3.27), Digestive diseases (OR =1.38, 95% CI: 1.07‐1.79) and musculoskeletal diseases (OR = 2.41, 95% CI: 1.79‐3.24) were significantly more likely to report CAD. CS with frequent intake of fish, Shrimp/crab/shell, milk and nuts \u0026ge;5 times/week had lower risk of CAD than those with intake of these less than 1 times/week. Decreased risk of CAD was found among those with higher scores of social support (OR =0.97, 95% CI: 0.96-0.99).\u003c/p\u003e\n\u003cp\u003eTransitional logistic regression and three machine learning methods were used to predict CAD for CS. The predictive performance ROC plots of training set and testing set were shown in Table 3. In the training set, the ROC curve shown that the diagnostic performance of the four logistic regression and machine learning algorithms to predict CAD ranked from high to low were RF, LR, SVM, and DT. In the testing set, RF classifier achieved the best prediction performance with 0.771, 0.701, 0.68 and 0.684 in AUC, accuracy (Acc.), and sensitivity (Sens.) and specificity (Spec.). Similar prediction performance was found in logistic regression with a sensitivity of 0.675, specificity of 0.693, classification accuracy of 0.689, and AUC of 0.769.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeature importance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 shown the feature importance ranking for predicting CAD in logistic regression and the three machine learning models. Seven of the thirteen contributing factors (gender, fish intake frequency, hypertension, digestive diseases, musculoskeletal diseases, stroke and social support) were identified in both logistic regression and machine learning models. Additionally, the RF model also put emphasis on age, marital status, working status, time since diagnosis and radiotherapy.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the association of comorbid anxiety and depression with various factors among and used different machine learning algorithms to predict CAD among 1546 Chinese cancer survivors. Our study indicated that the prevalence of CAD was 18.2%, and random forest (RF) and logistic regression achieved similar predictive performance with 0.771 and 0.769 in AUC in testing set, respectively. Social support and comorbid chronic diseases were important top ranking associated factors with CAD in all the predictive models.\u003c/p\u003e\n\u003cp\u003eOur results indicated that nearly one in five cancer survivors reported CAD which indicated that CAD had become a severe psychological distress and burden that faced by numerous CSs. As two negative psychological problems, anxiety and depression may decline body's immunity, and be associated with the development and progression of cancer. Anxiety and depression might also lead to lower adherence to treatment, higher suicide risk, and additional health expenditures (\u003ca href=\"#_ENREF_21\"\u003e21\u003c/a\u003e, \u003ca href=\"#_ENREF_22\"\u003e22\u003c/a\u003e). Compared with CS with simple anxiety or depression, CAD was associated with more complex symptom, and greater negative impact on the cancer treatment effect and CS\u0026rsquo; quality of life. It is important to found risk factors of CAD, reduce the prevalence of CAD, conduct psychological health promotion, and finally improve CS\u0026rsquo; psychological health.\u003c/p\u003e\n\u003cp\u003eMachine learning algorithms have several advantages beyond traditional statistical approaches in building predictive model (\u003ca href=\"#_ENREF_23\"\u003e23\u003c/a\u003e): no linear requirement for data distribution, discovery and utilization of the complex interaction and non-linear relationships among related factors automatically, and high tolerance to outliers and missing values. These advantages allowed machine learning techniques has high application value in predictive classification. In this study, we applied the several machine learning algorithms to develop predictive models to determine CSs at risk of CAD. Among the three machine learning algorithms, in external validation, RF shown the best prediction performance in the testing dataset with an accuracy of 68.4% (AUC = 0.771, 95%CI=0.714, 0.829). However, the performance of RF was similar to the performance of logistic regression (AUC = 0.769, 95%CI=0.711, 0.862). Our study indicated that although machine learning algorithms have the potential to identify cancer survivors at risk of CAD using questionnaire-based survey, however the difference in AUC between LR and random forest was not statistically significant. A previous systematic review indicated that clinical prediction models based on machine learning would not lead to better AUC than that based on LR(\u003ca href=\"#_ENREF_24\"\u003e24\u003c/a\u003e). Logistic regression is a powerful tool that easy to understand and implement, especially in epidemiologic studies. Future studies would possibly show a better performance if a single machine learning algorithm is combined with various algorithms rather than a single algorithm model.\u003c/p\u003e\n\u003cp\u003eMachine learning were considered as \u0026ldquo;black-box\u0026rdquo;, and had limitations to reveal how features are interacting and what effect they have on the outcome (\u003ca href=\"#_ENREF_15\"\u003e15\u003c/a\u003e, \u003ca href=\"#_ENREF_25\"\u003e25\u003c/a\u003e). However, the feature importance provided information evaluate the importance ranking of each factor in the classification decision. Six of thirteen selected features were comorbid chronic diseases. Our previous study had shown that comorbid chronic diseases was associated with greater probability of anxiety and depression(\u003ca href=\"#_ENREF_26\"\u003e26\u003c/a\u003e), which indicating that physical problem might had great influence on CSs\u0026rsquo; psychological health, and effectively monitoring and management of CCDs should be provided for CSs to address the psychological and psychical problems. Social support was another important predictor of CAD. Social support refers to the material and spiritual support that provided from family, friends and others. The association between social support and anxiety, depression among cancer patients had been proven in previous studies (\u003ca href=\"#_ENREF_27\"\u003e27\u003c/a\u003e, \u003ca href=\"#_ENREF_28\"\u003e28\u003c/a\u003e). Cancer may had negative influence on individuals\u0026rsquo; social relationships and the support the patient receives after cancer diagnosis. Social support may act as a coping resource that could deal with the side effect of cancer treatments and mitigate the adverse psychological issues.\u003c/p\u003e\n\u003cp\u003eSeveral limitations of our study should be acknowledged. First, due to the good reliability and validity of SAS and SDS in Chinese population (\u003ca href=\"#_ENREF_29\"\u003e29\u003c/a\u003e, \u003ca href=\"#_ENREF_30\"\u003e30\u003c/a\u003e), we used questionnaire-based scales to screen anxiety and depression rather than a clinical diagnosis of either. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM ‐5) should be as gold standard diagnostic instruments in further studies. Second, several important clinical indicators that may associated with psychological health, such as cancer stage and pathological type, were not collected in our study. The performance of our predictive model might be improved if we composed of more detailed basic questions. Third, larger sample size of CSs from multicenter should be included in the future study to verify and improve the stability and reliability of the current machine learning models. At last, the relationship between CAD and several factors, such as physical activity and comorbid chronic diseases, might be bidirectional. Given the cross-sectional nature of current study, we cannot deduce the causal relationships between associated factors and CAD.\u003c/p\u003e\n\u003cp\u003eSeveral clinical implication of our study should also be emphasized. A high percentage of Chinese CSs suffered from both anxiety and depression, and it could be beneficial to screen these psychological distress and improve CS\u0026rsquo; quality of life. However, in China, the cancer management is more focused on cancer treatment outcome, instead of psychological health. Psychological problems was infrequently discussed or treated, and remain on the periphery of cancer care. Both logistic regression and machine learning models indicated several intervention targets to improve CSs\u0026rsquo; psychological health, such as frequently intake of fish and egg, and effective treatment to control the coexisting disease, and provide more social support. Psychological care was strongly recommend to be incorporated into regular health care and management of cancer survivors. Additionally, the cooperation of oncologist, physician , psychologist, family and others were needed to provide supportive collaborative care.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study shown that machine learning techniques have the potential to identify Chinese cancer survivors at risk of CAD using questionnaire-based survey of socio-demographic factors, socioeconomic status, life behaviors, health conditions and social support. Random forest model and logistic regression have the similarly good model performance, and would facilitate early screening of high risk of CAD and changing modifiable risk factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements: \u003c/strong\u003eShanghai Cancer Rehabilitation Club provided invaluable resources for field investigation. We are grateful to all involved cancer survivors to participate in this study. We also thank all workers and volunteers involved in the acquisition of data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e\u0026nbsp;All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest: \u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch involving human participants\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe study was approved by the Medical Research Ethics Committee of the school of public health, Fudan University (The international registry NO. IRB00002408 \u0026amp;FWA00002399).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e\u0026nbsp;Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability: \u003c/strong\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors \u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRui Yan and Xiaoguang Yang proposed and designed the study. Rui Yan and Xiaoguang Yang built models and wrote the paper. Rui Yan and Xiaoguang Yang wrote the paper. Rui Yan and Jiwei Wang did ground work of collecting original data and revised the paper. Jinming Yu supervised the study and revised the paper. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"Reference","content":"\u003col\u003e\n\u003cli\u003eZeng H, Chen W, Zheng R, Zhang S, Ji JS, Zou X, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. The Lancet Global health. 2018;6(5):e555-e67.\u003c/li\u003e\n\u003cli\u003eDenlinger CS, Carlson RW, Are M, Baker KS, Davis E, Edge SB, et al. Survivorship: introduction and definition. Clinical practice guidelines in oncology. Journal of the National Comprehensive Cancer Network : JNCCN. 2014;12(1):34-45.\u003c/li\u003e\n\u003cli\u003eKhan NF, Evans J, Rose PW. A qualitative study of unmet needs and interactions with primary care among cancer survivors. Brit J Cancer. 2011;105:S46-S51.\u003c/li\u003e\n\u003cli\u003eMitchell AJ, Ferguson DW, Gill J, Paul J, Symonds P. Depression and anxiety in long-term cancer survivors compared with spouses and healthy controls: a systematic review and meta-analysis. Lancet Oncology. 2013;14(8):721-32.\u003c/li\u003e\n\u003cli\u003eLamers F, van Oppen P, Comijs HC, Smit JH, Spinhoven P, van Balkom AJ, et al. Comorbidity patterns of anxiety and depressive disorders in a large cohort study: the Netherlands Study of Depression and Anxiety (NESDA). J Clin Psychiatry. 2011;72(3):341-8.\u003c/li\u003e\n\u003cli\u003eDong L, Freedman VA, Mendes de Leon CF. The association of comorbid depression and anxiety symptoms with disability onset in older adults. Psychosom Med. 2019.\u003c/li\u003e\n\u003cli\u003eLowe B, Spitzer RL, Williams JB, Mussell M, Schellberg D, Kroenke K. Depression, anxiety and somatization in primary care: syndrome overlap and functional impairment. Gen Hosp Psychiatry. 2008;30(3):191-9.\u003c/li\u003e\n\u003cli\u003eWalker J, Hansen CH, Martin P, Symeonides S, Ramessur R, Murray G, et al. 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Biom J. 2014;56(4):588-93.\u003c/li\u003e\n\u003cli\u003eChristodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12-22.\u003c/li\u003e\n\u003cli\u003eWeng SF, Vaz L, Qureshi N, Kai J. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PloS one. 2019;14(3).\u003c/li\u003e\n\u003cli\u003eYan R, Xia J, Yang RR, Lv BH, Wu P, Chen WL, et al. Association between anxiety, depression, and comorbid chronic diseases among cancer survivors. Psycho-Oncology. 2019;28(6):1269-77.\u003c/li\u003e\n\u003cli\u003ede Tejada MGZ, Bilbao A, Bare M, Briones E, Sarasqueta C, Quintana JM, et al. Association between social support, functional status, and change in health-related quality of life and changes in anxiety and depression in colorectal cancer patients. Psycho-Oncology. 2017;26(9):1263-9.\u003c/li\u003e\n\u003cli\u003eManne SL, Kashy DA, Virtue S, Criswell KR, Kissane DW, Ozga M, et al. Acceptance, social support, benefit-finding, and depression in women with gynecological cancer. Qual Life Res. 2018;27(11):2991-3002.\u003c/li\u003e\n\u003cli\u003eTanakamatsumi J, Kameoka VA. Reliabilities and Concurrent Validities of Popular Self-Report Measures of Depression, Anxiety, and Social Desirability. J Consult Clin Psych. 1986;54(3):328-33.\u003c/li\u003e\n\u003cli\u003ePeng H ZY, Gi Y, Tang W, Li Q, Yan X. Analysis of reliability and validity of Chinese version SDS scale in women of rural area. Shanghai Med Pharm J. 2013;34(14):20-3.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";'\u003eTable 1 Prevalence of anxiety and depression among CSs by tumor site\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\n \u003ctable style=\"border: none;width:330.4pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width:100.75pt;border:none;border-top: solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:15.2pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";color:black;'\u003eCancer Site\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:96.85pt;border:none;border-top: solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:15.2pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";color:black;'\u003eNumber\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:132.8pt;border:none;border-top: solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:15.2pt;\"\u003e\n \u003cp 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style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";color:black;'\u003eAll others\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026nbsp;a\u003c/span\u003e\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:96.85pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:15.2pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";color:black;'\u003e207\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:44.35pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:15.2pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";color:black;'\u003e31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:88.45pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:15.2pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";color:black;'\u003e14.98%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eAbbreviations: CAD,\u003c/span\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";background:white;'\u003e\u0026nbsp;comorbid anxiety and depression\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u003csup\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003ea\u003c/span\u003e\u003c/sup\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026nbsp;All other cancer sites include kidney (N = 28), nasopharynx (N = 22), prostate (N = 21), bladder (N = 20), oral cavity (N = 12), esophagus (N = 10), pancreas (N = 10), gallbladder (N = 8), leukemia (N = 8), melanoma of skin (N = 7), larynx (N = 6), bone (N = 4), brain (N = 2), and other cancer sites (N = 28).\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size:15px;font-family:等线;\"\u003e\u003cbr\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style=\"font-size:15px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eTable 2 Prevalence of CAD in CS among various basic characters\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\n \u003ctable style=\"border: none;width:506.5pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width:131.35pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eCharacteristics\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:68.3pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eN(%)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:132.95pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eCAD\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:49.65pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e/t\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:48.7pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eP\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:75.55pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eOR(95%CI)\u003csup\u003ea\u003c/sup\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:24.35pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eNo\u003cbr\u003e\u0026nbsp;N(%)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:64.65pt;padding:0in 5.4pt 0in 5.4pt;height:24.35pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eYes\u003cbr\u003e\u0026nbsp;N(%)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131.35pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eGender\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:68.3pt;border:none;border-top:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:64.65pt;border:none;border-top:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e415(26.84)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.3pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e369(88.92)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64.65pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e46(11.08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eref\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eFemale\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e1131(73.16)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.3pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e895(79.13)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64.65pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e236(20.87)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e19.48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family: \"Times New Roman\",\"serif\";color:black;'\u003e\u0026lt;.001\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e2.12(1.51,2.97)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131.35pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eAge (years)\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:64.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026lt;50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp 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style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026nbsp;\u0026lt;senior high school\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e865(55.95)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e700(80.92)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:64.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e165(19.08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eref\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026nbsp;senior high school\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e504(32.6)\u003c/span\u003e\u003c/p\u003e\n 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5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.96(0.72,1.27)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026nbsp;\u0026gt;senior high school\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New 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style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e3.02\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.221\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.67(0.42,1.06)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131.35pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHousehold per capita \u003cbr\u003e\u0026nbsp;income (yuan/month)\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:64.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 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style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e221(79.50)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:64.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e57(20.50)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eref\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026nbsp;2000~ \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e677(43.79)\u003c/span\u003e\u003c/p\u003e\n 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5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.80(0.56,1.14)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026ge;\u003c/span\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e4000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan 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style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e46(73.02)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:64.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e17(26.98)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp 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5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e1.20(0.89,1.61)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026nbsp;Obese (\u003c/span\u003e\u003cspan style='font-size:13px;font-family:\"Times New 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style=\"width:75.55pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e1.30(0.69,2.45)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131.35pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eTime since diagnosis (years)\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 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style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e86(5.56)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e69(80.23)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:64.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e17(19.77)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 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style=\"width: 68.3pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e385(24.9)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.3pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e320(83.12)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64.65pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New 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style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e88(16.99)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75.55pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.84(0.58,1.20)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp 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64.65pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e73(18.43)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75.55pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.92(0.63,1.34)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:131.35pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e\u0026ge;5 times/week\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.3pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e312(20.18)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.3pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e254(81.41)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64.65pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e58(18.59)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e1.03\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:48.7pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.794\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75.55pt;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.93(0.63,1.39)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131.35pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 14.75pt;vertical-align: bottom;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSocial support score\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.3pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:68.3pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e66.18\u0026plusmn;10.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:64.65pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e62.64\u0026plusmn;11.35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.65pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:48.7pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family: \"Times New Roman\",\"serif\";color:black;'\u003e\u0026lt;.0001\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:75.55pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:14.75pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family: \"Times New Roman\",\"serif\";color:black;'\u003e0.97(0.96,0.99)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eAbbreviations: CAD,\u003c/span\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";background:white;'\u003e\u0026nbsp;comorbid anxie\u003c/span\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003ety and depression; BMI, body mass index; TMC, traditional Chinese medicine\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u003csup\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003ea\u003c/span\u003e\u003c/sup\u003e \u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eadjusted for all other factors\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size:14px;font-family:等线;\"\u003e\u003cbr\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",\"serif\";'\u003eTable 3 The predictive performance of each model\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\n \u003ctable style=\"width: 4.8e+2pt;border: none;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:40.75pt;border:none;border-top:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.65pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width:212.3pt;border:none;border-top: solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.65pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eTraining set\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:16.3pt;border:none;border-top:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.65pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size:13px;color:black;\"\u003e \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width:212.45pt;border:none;border-top: solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.65pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eTesting set\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:40.75pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:40.8pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family: \"Times New Roman\",\"serif\";color:black;'\u003eSens\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:40.8pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family: \"Times New Roman\",\"serif\";color:black;'\u003eSpec\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:40.75pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\n \u003cp 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style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.839(0.812-0.869)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:16.3pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:40.8pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";'\u003e0.701\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:40.8pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";'\u003e0.68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:40.75pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";'\u003e0.684\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:89.8pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:3.95pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e0.771(0.714-0.829)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border:none;padding:0in 0in 0in 0in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eAbbreviations: Sens, sensitivity; Spec, specificity; Acc, accuracy; AUC, the area under the receiver operating characteristics curve; LR, logistic regression; RF, Random Forest; DT, Decision Tree; SVM, Support Vector Machine\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",\"serif\";'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\",\"serif\";'\u003eTable 4 Feature importance ranking for predicting CAD in training set\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\n \u003ctable style=\"border: none;width:454.6pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:20.5pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eFeature Importance\u003cbr\u003e\u0026nbsp;Ranking\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:20.5pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eLR\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:20.5pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eRF\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:20.5pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eDT\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;height:20.5pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cstrong\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSVM\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eMusculoskeletal diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eCancer site\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eMusculoskeletal diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHypertension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eFish intake frequency\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHeart disease\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eCancer site\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHyperlipidemia\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eGender\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eMusculoskeletal diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eGender\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eGender\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSocial Support\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSocial Support\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSmoking\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eMusculoskeletal diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eStroke\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eFish intake frequency\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHyperlipidemia\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eFish intake frequency\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eMarital status\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSmoking\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eFish intake frequency\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSmoking\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eAge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eGender\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eStroke\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eEgg intake frequency\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003ework\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eDigestive diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eRespiratory diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eCancer site\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eTime since diagnosis\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eStroke\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSocial Support\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHeart disease\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eFruit intake frequency\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eEgg intake frequency\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHypertension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eRespiratory diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHypertension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHypertension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHeart disease\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eStroke\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eDigestive diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eRespiratory diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eEgg intake frequency\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eDigestive diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:69.25pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:center;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003e13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:98.25pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eRadiotherapy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eHyperlipidemia\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eDigestive diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:95.7pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:.1in;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:11px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eSocial Support\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:等线;\"\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",\"serif\";color:black;'\u003eAbbreviations: LR, logistic regression; RF, Random Forest; DT, Decision Tree; SVM, Support Vector Machine\u0026nbsp;\u003c/span\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"anxiety, depression, cancer survivor, machine learning, prediction model\t\t\t\t\t\t","lastPublishedDoi":"10.21203/rs.3.rs-32449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-32449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWe aimed to investigate the prevalence and associated factors of comorbid anxiety and depression (CAD) in Chinese cancer survivors (CS), and develop novel prediction model using traditional logistic regression and machine-learning algorithms to predict CS\u0026rsquo; CAD, which would be helpful to accurately distinguish individuals with high risk of psychological problems.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study of 1546 CSs were conducted in Shanghai China. Self-reported structured questionnaire was used to collect information about basic socio-demographic factors, socioeconomic status, life behavior, health conditions, fatigue, social support, anxiety (Zung self-rating anxiety scale) and depression (Zung self - rating depression scale). Stepwise logistic regression and three machine learning algorithms (support vector machine, decision tree, and random forest) were used to construct the prediction model of CS\u0026rsquo; CAD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e18.24% CSs reported CAD. The AUC of models to predict the high-risk CAD in the training set was best in that of RF (0.839), followed by LR (0.811), SVM (0.808), and DT (0.794). Social support and several comorbid chronic diseases were important top ranking contributing factors of CAD in all the predictive models.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCAD was a common psychological problems in cancer survivors (CSs).The machine leaning techniques and logistic regression showed similar performance to classify Chinese CSs at risk of CAD using questionnaire-based survey. Effective treatment to control the coexisting disease, more social support should be provided to CSs to improve psychological health. Psychological care was recommend to be incorporated into regular health care and management of cancer survivors.\u003c/p\u003e","manuscriptTitle":"Prediction of comorbid anxiety and depression using machine learning models in cancer survivors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-06-11 16:43:02","doi":"10.21203/rs.3.rs-32449/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"784a9d2e-5909-4409-864f-9f049ddaa89d","owner":[],"postedDate":"June 11th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":117863,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2020-07-10T17:03:56+00:00","versionOfRecord":[],"versionCreatedAt":"2020-06-11 16:43:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-32449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-32449","identity":"rs-32449","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}