Developing a Logistic Regression-Based Scoring Tool for Coronary Heart Disease Using the Cleveland Dataset: A Methodological Study

Developing a Logistic Regression-Based Scoring Tool for Coronary Heart Disease Using the Cleveland Dataset: A Methodological Study | 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 Developing a Logistic Regression-Based Scoring Tool for Coronary Heart Disease Using the Cleveland Dataset: A Methodological Study Heba Ramadan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7813237/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 This study proposes a methodological approach for transforming statistical models into a practical screening tool for coronary heart disease (CHD). The Cleveland Heart Disease dataset, which includes 13 clinical and demographic variables, was analyzed using logistic regression. Two models were developed: a binary logistic regression to predict the presence or absence of CHD, and an ordinal logistic regression to examine disease severity across angiographic categories. Regression coefficient were then adapted into a simplified points-based system, suitable for implementation in quiz-style Google Forms. Each response option was assigned a weighted score, enabling automated calculation of risk upon completion of the form. The aim was to create an educational, doctor-oriented tool that highlights short-term risk factors and provides immediate feedback on both disease presence and potential severity. While this approach demonstrates how statistical modeling can be translated into an interactive and user-friendly format, limitations include the modest size and the historical nature of the Cleveland dataset, and the absence of external validation. The tool is not proposed as a validated instruments but as an example of how statistical models can be transformed into interactive and user-friendly screening formats for educational use. Cleveland dataset logistic regression coronary heart disease screening tool questionnaire 1. Introduction Coronary heart disease (CHD) remains one of the leading causes of morbidity and mortality worldwide, and its early detection continue to be a challenge for both clinicians and health systems (Celermajer David, Chow Clara, Marijon, Anstey Nicholas, & Woo Kam, 2012). A variety of risk prediction models and questionnaires have been developed over the years to support screening and guide preventive interventions. These models often rely on long-term cohort data and are designed for predicting outcomes such as 10-year cardiovascular risk. While clinically valuable, such approaches may not directly address short-term diagnostic or educational needs. The Heart Disease dataset collection provides a unique opportunity for developing alternative approaches. This collection comprises four subsets (Cleveland, Hungary, Switzerland, and AV Long Beach), each based on patients undergoing coronary angiography. Among them, the Cleveland dataset has become the most frequently used in research, largely because it contains 303 well-documented cases with minimal missing values and higher internal consistency compared with the other subsets. According to the UCI Machine Learning Repository, it has been cited more than 60 times and viewed thousands of times as a benchmark dataset. however, in nearly all these instances it has served primarily as a testbed for statistical and machine learning algorithms, with little emphasis on its medical context or potential application in clinical education ( Janosi, A., Steinbrunn, W., Pfisterer, M., & Detrano, R., 1989 ). Revisiting the Cleveland dataset from a medical perspective offers an opportunity to bridge this gap. By applying logistic regression to all 13 variables and translating the results into a scoring-based questionnaire, it is possible to demonstrate how traditional statistical modeling can be repurposed into a practical screening and educational tool. Such as approach may provide doctors with a simple way to explore the short-term correlates of CHD, while also serving as a proof of concept for integrating data-driven methods into user-friendly formats. 2. Methods 2.1. Data sources Data were obtained from the Cleveland Heart Disease dataset, which includes 303 patients evaluated by coronary angiography. The primary outcome was the presence of coronary heart disease, defined as ≥ 50% narrowing in at least one major coronary vessel. In addition, disease severity was coded on an ordinal 0–4 scale, where 0 represented no disease, 1 mild disease (one vessel affected), 2 moderate disease (two vessels affected), 3 significant disease (three vessels affected), and 4 severe disease (four vessels affected, including cases with left main coronary artery stenosis). 2.2. Independent variables Thirteen variables were considered: age: in years sex: (0 = female, 1 = male) Cp: chest pain type (1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic) trestbps: resting systolic blood pressure (mmHg) chol: serum cholesterol (mg/dl) fbs: fasting blood sugar (1 = having fasting blood sugar > 120 mg/dl, 0 = normal blood sugar) restecg: resting electrocardiographic results (0 = normal, 1 = ST-T abnormality, 2 = left ventricular hypertrophy) thalach: maximum heart rate achieved (measured during exercise) bpm (beats per minute) exang: exercise-induced angina (0 = no, 1 = yes) oldpeak: ST depression induced by exercise relative to rest (0 = no ST depression, 0–1 mm = healthy individuals, ≥ 1 mm suggestive for ischemia, ≥ 2 mm strong indication of coronary artery disease) slope: slope of peak exercise ST segment (1 = upsloping; normal, 2 = flat; early sign of ischemia, 3 = down-sloping; strongly associated with myocardial ischemia or coronary artery disease) ca: number of major vessels colored by fluoroscopy (0–3) thal: thalassemia status (3 = normal, 6 = fixed defect, 7 = reversible defect) 2.3. Statistical analysis All analyses were conducted using IBM SPSS 25. Two models were applied: Binary logistic regression with dependent variables coded as 0 (no disease) versus 1 (disease present, categories 1–4 combined). Ordinal logistic regression with dependent variable coded on the original 0–4 severity scale All 13 variables were entered as independent predictors. For categorical variables, dummy coding was applied. Model performance was evaluated using goodness-of-fit statistics, odds ratios, and predictive accuracy measures. Regression coefficients were then rescaled into integer points to create a simplified scoring system suitable for implementation in an interactive questionnaire. This scoring system was designed to provide immediate feedback regarding the presence of coronary heart disease. 2.4. Questionnaire development Regression-based weights were adapted into a simplified scoring system. Each variable response was assigned a point value, and total scores were divided into categories representing disease risk. The system was implemented in quiz-style Google Forms, allowing automatic scoring and immediate feedback. 3. Results 3.1. Descriptive statistics Among the 303 participants, about two-third were male (68.0%), while females represented 32.0%. The mean age was 54.4 ± 9.0 years (range: 29–77). Nearly half of the sample (47.5%) reported asymptomatic chest pain, whereas 28.4% experienced non-anginal pain, 16.5% atypical angina, and only 7.6% typical angina. Most participants had normal fasting blood sugar levels (85.1%), with 14.9% showing elevated levels (> 120 mg/dl). Resting electrocardiographic results were almost equally distributed between normal findings (49.8%) and left ventricular hypertrophy (48.8%), while ST-T abnormalities were uncommon (1.3%). The mean resting systolic blood pressure was 131.7 ± 17.6 mmHg (range: 94–200). Categorically, 44.6% had normal systolic pressure (< 130 mmHg), 23.1% had stage 1 hypertension (130–139 mmHg), and 32.3% had stage 2 hypertension (≥ 140 mmHg). Mean serum cholesterol was 246.7 ± 51.8 mg/dl, with 16.2% <200 mg/dl, 32.3% between 200–239 mg/dl, and 51.5% ≥240 mg/dl. Maximum heart rate averaged 149 ± 22.9 bpm (range: 71–202). The mean ST-segment depression during exercise was 1.04 ± 1.16 mm, with 32.7% showing no depression, 22.1% with < 1 mm, and 45.2% with ≥ 1 mm depression. Exercise-induced angina was reported in about one-third of patients (32.7%), while the majority did not experience angina on exertion (67.3%). The slope of the peak exercise ST segment was either upsloping (46.9%) or flat (46.2%) in most patients, with only 6.9% demonstrating the clinically adverse down-sloping pattern. Fluoroscopy revealed no major vessels colored in 179 patients (59.1%), while one, two, and three vessels were colored in 66 (21.8%), 38 (12.5%), and 20 (6.6%) patients, respectively. Thalassemia-related results showed that 55.1% of participants were classified as normal, 38.9% exhibited a reversible defect, and 5.9% a fixed defect. Regarding coronary heart disease (CHD) outcomes, 54.1% had no angiographic evidence of disease, while 45.9% had some degree of disease. On the ordinal severity scale, 18.2% had mild, 11.9% moderate, 11.6% significant, and 4.3% severe disease (Tables 1 , 2 ). Table 1 Descriptive statistics of categorical variables in the Cleveland Heart Disease dataset (N = 303) Variable Category Frequency (n) Percent (%) Sex Female 97 32.0 Male 206 68.0 Chest pain type Typical angina 23 7.6 Atypical angina 50 16.5 Non-anginal pain 86 28.4 Asymptomatic 144 47.5 Fasting blood sugar Normal ( 120 mg/dl) 45 14.9 Resting ECG results Normal 151 49.8 ST-T abnormality 4 1.3 Left ventricular hypertrophy 148 48.8 Resting systolic blood pressure Normal 135 44.6 Stage 1 hypertension 70 23.1 Stage 2 hypertension 98 32.3 Serum cholesterol normal 49 16.2 elevated 98 32.3 High levels 156 51.5 ST depression No ST-depression 99 32.7 0–1 mm 67 22.1 ≥ 1 mm 137 45.2 Exercise-induced angina No 204 67.3 Yes 99 32.7 Slope of ST-segment Upsloping (normal) 142 46.9 Flat 140 46.2 Down-sloping 21 6.9 Thalassemia status Normal 167 55.1 Fixed defect 18 6.0 Reversible defect 118 38.9 No. of major vessels (fluoroscopy) 0 179 59.1 1 66 21.8 2 38 12.5 3 20 6.6 CHD severity (0–4 scale) No disease (0) 164 54.1 Mild (1 vessel) 55 18.2 Moderate (2 vessels) 36 11.9 Significant (3 vessels) 35 11.6 Severe (4 vessels) 13 4.3 CHD presence (binary) Absent 164 54.1 Present 139 45.9 Table 2 Descriptive statistics of continuous variables Variable Mean ± SD Median Minimum Maximum Age (years) 54.44 ± 9.04 56.00 29 77 Resting systolic blood pressure mmHg 131.69 ± 17.60 130 94 200 Serum cholesterol mg/dl 246.69 ± 51.78 241 126 564 Maximum heart rate achieved bpm 149.61 ± 22.88 153 71 202 ST depression mm 1.04 ± 1.16 0.8 0 6.2 No. of major vessels (fluoroscopy) 0.67 ± 0.93 0 0 3 3.2. Logistic regression 3.2.1. Assumption checks Prior to regression analysis, data were screened for suitability (Schober & Vetter, 2021 ). All the categorical predictors and dependent variables were pre-coded in SPSS v25, and no missing values were present in the dataset. the independence of observations was assumed, as each case represented a unique patient. For continuous variables (age, resting blood pressure, serum cholesterol, maximum heart rate, and ST-segment depression), the linearity of the logit was assessed using the Box-Tidwell approach. None of the interaction terms were statistically significant (p > 0.05), indicating that the assumption of linearity was met. Multicollinearity among predictors was assessed using variance inflation factors (VIF) derived from an auxiliary linear regression. All predictors showed tolerance values greater than 0.4 and VIF values below 2.5, confirming the absence of problematic multicollinearity among predictors. 3.2.2. Binary logistic regression The overall binary logistic regression model fit was highly satisfactory. The Omnibus test of model coefficients indicated that the logistic regression model significantly improved prediction compared to the null model (χ²(18) = 224.197, p < 0.001), confirming that the included predictors collectively explained the presence of coronary heart disease. The Cox& Snell R² was 0.523 and a Nagelkerke R² was 0.699, suggesting that the predictors explained approximately 52% to 70% of the variance in disease status. Model calibration was also acceptable, as evidenced by the Hosmer-Lemeshow test (χ²(8) = 7.093, p = 0.527), which indicated no evidence of poor fit. The classification table demonstrated excellent discriminatory performance, with an overall accuracy of 87.5%, sensitivity of 82.7%, and specificity of 91.5%. The reference groups for categorical variables were: asymptomatic (chest pain type), reversible defect (thalassemia status), left ventricular hypertrophy (resting ECG results), down-sloping (ST segment slope), yes (exercise-induced angina), high blood sugar (fasting blood sugar), and male (sex). Significant predictors of CHD were sex, chest pain type, systolic blood pressure, number of major vessels, and thalassemia status. Sex: females had 78.3% lower odds of CHD compared to males (B=-1.526, OR = 0.217, p = 0.004). Chest pain: compared to asymptomatic patients, those with typical angina had 87.9% lower odds (B= -2.11, OR = 0.121, p = 0.001), and those with anginal pain had 84.7% lower odds (B=-1.876, OR = 0.153, p < 0.001). Resting systolic blood pressure: each 1 mmHg increase was associated with a 2.5% increase in the odds of CHD (B = 0.024, OR = 1.025, p = 0.031). Number of major vessels: each additional vessel increased the odds of CHD by 271.2% (B = 1.312, OR = 3.712, p < 0.001). Thalassemia status: patients with normal perfusion had 74.7% lower odds of CHD compared to those with a reversible defect (B= -1.373, OR = 0.253, p = 0.001). 3.2.3. Ordinal logistic regression An ordinal logistic regression model was performed to identify predictors of CHD severity, which was categorized as no disease (54.1%), mild (18.2%), moderate (11.9%), significant (11.6%), and severe (4.3%). Among the clinical variables, the number of major vessels visualized by fluoroscopy (Estimate = 0.890, p < 0.001) was the strongest predictor, with higher vessel counts markedly increasing the likelihood of progressing to more severe CHD categories. Chest pain type was also significantly associated with CHD severity: compared to asymptomatic patients, those presenting with typical angina (Estimate= -1.715, p = 0.002), atypical angina (Estimate = -1.090, p = 0.023), and non-anginal pain (Estimate= -1.536, p < 0.001) had significantly lower odds of being in a higher severity category. Male sex was independently associated with more severe (Estimate= -0.997, p = 0.006). thalassemia status showed a strong relationship with severity, where a reversible perfusion defect was associate with greater odds of more severe disease compared to normal scan (Estimate=-1.310, p < 0.001). Resting systolic blood pressure (B = 0.013, p = 0.099), ST-segment depression during exercise (oldpeak, Estimate = 0.276, p = 0.054), and thalassemia with fixed defect (Estimate= -0.946, p = 0.058) approached statistical significance. Other variables, including age, serum cholesterol, fasting blood sugar, maximum heart rate, resting ECG results, exercise-induced angina, and slope of the ST segment, did not show significant associations. 3.3. Development of a scoring system from ordinal logistic regression Based on the results of the ordinal logistic regression model, the significant or approached significance predictors of CHD severity were identified as number of major vessels visualized by fluoroscopy, chest pain type, sex, thalassemia status, Resting systolic blood pressure, and ST-segment depression during exercise. To translate these findings into a practical scoring system, regression coefficients were rescaled into integer points to reflect their relative contribution to CHD severity. Male sex was assigned + 3 points, while females received 0 points. For chest pain type, asymptomatic patients had the highest risk and were assigned + 6 points, whereas typical angina was scored 0, atypical angina + 1, and non-anginal pain + 1. Thalassemia status was scored as reversible defect + 4 points (highest risk), fixed defect + 1, and normal perfusion 0. The number of major vessels was weighted as + 3 points for each affected vessel (range 0–9). Points in the scoring system were assigned by approximating each 0.3 increase in the log-odds (B coefficient) from the ordinal logistic regression as one point. In this way, stronger predictors with larger B coefficient contributed proportionally more points to the total score. For categorical variables, the reference category was always assigned zero points, and the other categories were scored relative to it. For SBP (B = 0.013, p = 0.099); each 1 mmHg increase adds 0.013 to the log-odds. To translate this into the scoring system (≈ 1 point for every 0.3 log-odds), we need about 23 mmHg (0.3/0.013 ≈ 23) to each 1 point. For simplicity, it was rounded to 20 mmHg = 1 point. For oldpeak (B = 0.276, p = 0.054); each 1 unit increase adds 0.276 to the log-odds. That’s already very close to 0.3, so it naturally translates to 1 point per 1 unit increase. This produced a total score range from 0 (lowest risk: female, typical angina, normal perfusion, no vessel involvement, resting systolic blood pressure < 120 mmHg, ST-depression during exercise 160 mmHg, ST-depression during exercise > 3.5mm) (Table 3 ). Table 3 Weighted scoring system for predictors of cardiovascular risk Predictor Category Points Sex Female 0 Male + 3 Chest pain type Typical angina 0 Atypical angina + 1 Non-anginal pain + 1 Asymptomatic + 6 Thalassemia status Normal perfusion 0 Fixed defect + 1 Reversible defect + 4 Number of major vessels 0 0 1 + 3 2 + 6 3 + 9 Resting systolic blood pressure mmHg 160 3 ST-depression during exercise mm 3.5 3 After applying this scoring system to the 303 patients, the score distribution within each diagnostic was examined. Patients with no disease had a mean score of 6.64 and a median of 6.00 (range: 1–22). Those with mild disease had a mean of 12.58 and a median of 12.00 (range: 4–24), while moderate disease corresponded to a mean of 16.44 and a median of 17.00 (range: 9–24). For significant disease, the mean score was 17.63 and the median was 17.00 (range: 8–27), and patients with severe disease had the highest score, with a mean of 18.82 and a median of 19.00 (range: 10–25). Using these observed distributions, cutoff points were derived to reflect the progression of CHD severity: scores of 0–9 were categorized as no disease, 10–13 as mild disease, 14–16 as moderate disease, 17–18 as significant disease, and 19–28 as severe disease. These cutoffs were therefore determined empirically from the patient data, ensuring that the scoring system align with the actual distribution of disease categories in the study population (Table 4 ). Table 4 Disease severity classification based on total score Category Cutoff point No disease 0–9 Mild disease 10–13 Moderate disease 14–16 Significant disease 17–18 Severe disease 19–28 The significant variables and categories have been converted into questions in this Google Form: https://docs.google.com/forms/d/e/1FAIpQLSfT1-pi_WM5lSH0OwNkzn-Iu1SHajNxaMm00kivlzpPkhbTOg/viewform . Each choice is assigned a weight or point value. Google Forms, by default, does not support weighted questions, so the grades or final scores are not displayed immediately after submission. However, there are add-ons for Google Forms, such as Formfacade, that can be linked to the form to provide immediate, interactive, and user-friendly results. 4. Discussion This study illustrates how logistic regression model can be transformed into interactive questionnaires for educational use in CHD screening. The binary model allows distinction between individuals with and without disease, while the ordinal model provides additional insights into severity categories. Embedding the scoring system into Google Forms demonstrates how statistical results can be translated into practical, user-friendly formats accessible to doctors in routine settings. Compared with established tools such as the Framingham Risk Score and QRISK, the proposed questionnaire differs in both scope and intention. Framingham and QRISK are validated risk calculators designed to estimate long-term cardiovascular risk. In contrast, this tool is exploratory, based on a smaller dataset, and targets short-term indicators of angiographically defined CHD. Its primary value lies in demonstrating methodological feasibility rather than replacing existing instruments. Key limitations of this study should be acknowledged. First, the dataset itself is modest in size, with only 303 patients, and originates from 1980s. as a historical dataset, it reflects the medical practices and population characteristics of that era, which may differ from contemporary lifestyles, risk factor distributions, and diagnostic standards. Second, regression coefficients were simplified into integer points to allow scoring, a step that may reduce precision. Third, the model has not undergone external validation which restricts its generalizability. Finally, several predictors in the Cleveland dataset require specialized clinical testing (e.g., exercise ECG, fluoroscopy), limiting the practicality of the derived tool outside professional or research contexts. 5. Conclusion Transforming logistic regression models into questionnaire-based scoring systems provides a practical example of how statistical results can be applied in user-friendly formats. While this tool is limited by data size, simplification, and lack of validation, it serves as a proof of concept that bridges statistical modeling with interactive screening approaches. Declarations Ethics Approval: Ethical approval was not required for this study as it used publicly available, de-identified data from the UCI Machine Learning Repository. Consent for Publication: Not applicable. The study used publicly available, anonymized data and did not involve human participants. Conflict of interest No conflict of interest Funding sources No funding was received for this research Acknowledgement Nil References Celermajer David S, Chow Clara K, Marijon E, Anstey Nicholas M, Kam W, S (2012) Cardiovascular Disease in the Developing World. JACC 60(14):1207–1216. https://doi:10.1016/j.jacc.2012.03.074 Janosi A, Steinbrunn W, Pfisterer M, Detrano R (1989) Heart Disease [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C52P4X Schober P, Vetter TR (2021) Logistic Regression in Medical Research. Anesth Analgesia 132(2):365–366. https://doi:10.1213/ane.0000000000005247 Additional Declarations The authors declare no competing interests. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7813237","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526865595,"identity":"2c490a58-7ef0-4e75-91c1-500b14a7438b","order_by":0,"name":"Heba Ramadan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABIklEQVRIiWNgGAWjYBACAwYeKOsAEH/gsZFjbG9gYCZaC+MMmTRj5p4DJGhh5rE5nNg+IwG/FnP23oOfK2q2yfMdP3uAgScnzZh35hvDzwUVNgz87d0J2LRY9pxLljxz7LbhzDN5CQwSZ2zkJGfnGEvPOJMG5JzdgNVhN3IMJBvYbjNuOJBjwGDYk2ZsODvHQJq37TCDgUQudi333xj/bPh3237D+TcGDIn/Difuv3nG+DdeLTd4zCQb224nbgBax3CA53Bi4wweM/y2nMlLs2zsu50888YbA8YGnjRjxp60MmueM2k8OP1y/Ozhmw3fbtv2nc8xYP4DjsrDm2/zVNjI8bf3YtWCDNh/QGgOAxDJg08phtYHpKgeBaNgFIyC4Q8APOVr4NxPp4EAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8417-2509","institution":"Pharmacy Department, Agamy Medical District, Ministry of Health and Population, Agamy, Alexandria, Egypt","correspondingAuthor":true,"prefix":"","firstName":"Heba","middleName":"","lastName":"Ramadan","suffix":""}],"badges":[],"createdAt":"2025-10-09 05:34:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-7813237/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7813237/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93806646,"identity":"db1b73f6-8f22-4743-9b39-d3bb31cb5752","added_by":"auto","created_at":"2025-10-17 18:21:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7813237/v1/3e961f9f738fc7f4827d9dfa.docx"},{"id":93806647,"identity":"32a6ebc9-cbd0-4fe3-9b92-6a1788f298b9","added_by":"auto","created_at":"2025-10-17 18:21:31","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs7813237.json","url":"https://assets-eu.researchsquare.com/files/rs-7813237/v1/ee2702c5598b6a8494e45cf5.json"},{"id":93807141,"identity":"58d1b78e-d9a7-4961-850c-3c2f02204c85","added_by":"auto","created_at":"2025-10-17 18:29:31","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55209,"visible":true,"origin":"","legend":"","description":"","filename":"rs78132370enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7813237/v1/c572b3705454473141bbee1b.xml"},{"id":93807142,"identity":"7c7df489-c65b-4d43-b3e1-6f815466c1c8","added_by":"auto","created_at":"2025-10-17 18:29:31","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53734,"visible":true,"origin":"","legend":"","description":"","filename":"rs78132370structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7813237/v1/fd3c33111a7a1e14358ed653.xml"},{"id":93806649,"identity":"3faa66e5-0d7d-4499-8081-3850380aa5ca","added_by":"auto","created_at":"2025-10-17 18:21:31","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57430,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7813237/v1/46487cde863f3c9abe42828a.html"},{"id":93807468,"identity":"93730321-2331-4522-b123-468f95c06740","added_by":"auto","created_at":"2025-10-17 18:37:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":662055,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7813237/v1/4043f912-cbbd-423e-87f2-dcc34b865db8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDeveloping a Logistic Regression-Based Scoring Tool for Coronary Heart Disease Using the Cleveland Dataset: A Methodological Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoronary heart disease (CHD) remains one of the leading causes of morbidity and mortality worldwide, and its early detection continue to be a challenge for both clinicians and health systems (Celermajer David, Chow Clara, Marijon, Anstey Nicholas, \u0026amp; Woo Kam, 2012). A variety of risk prediction models and questionnaires have been developed over the years to support screening and guide preventive interventions. These models often rely on long-term cohort data and are designed for predicting outcomes such as 10-year cardiovascular risk. While clinically valuable, such approaches may not directly address short-term diagnostic or educational needs.\u003c/p\u003e\u003cp\u003eThe Heart Disease dataset collection provides a unique opportunity for developing alternative approaches. This collection comprises four subsets (Cleveland, Hungary, Switzerland, and AV Long Beach), each based on patients undergoing coronary angiography. Among them, the Cleveland dataset has become the most frequently used in research, largely because it contains 303 well-documented cases with minimal missing values and higher internal consistency compared with the other subsets. According to the UCI Machine Learning Repository, it has been cited more than 60 times and viewed thousands of times as a benchmark dataset. however, in nearly all these instances it has served primarily as a testbed for statistical and machine learning algorithms, with little emphasis on its medical context or potential application in clinical education ( Janosi, A., Steinbrunn, W., Pfisterer, M., \u0026amp; Detrano, R., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRevisiting the Cleveland dataset from a medical perspective offers an opportunity to bridge this gap. By applying logistic regression to all 13 variables and translating the results into a scoring-based questionnaire, it is possible to demonstrate how traditional statistical modeling can be repurposed into a practical screening and educational tool. Such as approach may provide doctors with a simple way to explore the short-term correlates of CHD, while also serving as a proof of concept for integrating data-driven methods into user-friendly formats.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data sources\u003c/h2\u003e\u003cp\u003eData were obtained from the Cleveland Heart Disease dataset, which includes 303 patients evaluated by coronary angiography. The primary outcome was the presence of coronary heart disease, defined as \u0026ge;\u0026thinsp;50% narrowing in at least one major coronary vessel. In addition, disease severity was coded on an ordinal 0\u0026ndash;4 scale, where 0 represented no disease, 1 mild disease (one vessel affected), 2 moderate disease (two vessels affected), 3 significant disease (three vessels affected), and 4 severe disease (four vessels affected, including cases with left main coronary artery stenosis).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Independent variables\u003c/h2\u003e\u003cp\u003eThirteen variables were considered:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eage: in years\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003esex: (0\u0026thinsp;=\u0026thinsp;female, 1\u0026thinsp;=\u0026thinsp;male)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCp: chest pain type (1\u0026thinsp;=\u0026thinsp;typical angina, 2\u0026thinsp;=\u0026thinsp;atypical angina, 3\u0026thinsp;=\u0026thinsp;non-anginal pain, 4\u0026thinsp;=\u0026thinsp;asymptomatic)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003etrestbps: resting systolic blood pressure (mmHg)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003echol: serum cholesterol (mg/dl)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003efbs: fasting blood sugar (1\u0026thinsp;=\u0026thinsp;having fasting blood sugar\u0026thinsp;\u0026gt;\u0026thinsp;120 mg/dl, 0\u0026thinsp;=\u0026thinsp;normal blood sugar)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003erestecg: resting electrocardiographic results (0\u0026thinsp;=\u0026thinsp;normal, 1\u0026thinsp;=\u0026thinsp;ST-T abnormality, 2\u0026thinsp;=\u0026thinsp;left ventricular hypertrophy)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ethalach: maximum heart rate achieved (measured during exercise) bpm (beats per minute)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eexang: exercise-induced angina (0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eoldpeak: ST depression induced by exercise relative to rest (0\u0026thinsp;=\u0026thinsp;no ST depression, 0\u0026ndash;1 mm\u0026thinsp;=\u0026thinsp;healthy individuals, \u0026ge;\u0026thinsp;1 mm suggestive for ischemia, \u0026ge;\u0026thinsp;2 mm strong indication of coronary artery disease)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eslope: slope of peak exercise ST segment (1\u0026thinsp;=\u0026thinsp;upsloping; normal, 2\u0026thinsp;=\u0026thinsp;flat; early sign of ischemia, 3\u0026thinsp;=\u0026thinsp;down-sloping; strongly associated with myocardial ischemia or coronary artery disease)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eca: number of major vessels colored by fluoroscopy (0\u0026ndash;3)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ethal: thalassemia status (3\u0026thinsp;=\u0026thinsp;normal, 6\u0026thinsp;=\u0026thinsp;fixed defect, 7\u0026thinsp;=\u0026thinsp;reversible defect)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted using IBM SPSS 25. Two models were applied:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBinary logistic regression with dependent variables coded as 0 (no disease) versus 1 (disease present, categories 1\u0026ndash;4 combined).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOrdinal logistic regression with dependent variable coded on the original 0\u0026ndash;4 severity scale\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eAll 13 variables were entered as independent predictors. For categorical variables, dummy coding was applied. Model performance was evaluated using goodness-of-fit statistics, odds ratios, and predictive accuracy measures. Regression coefficients were then rescaled into integer points to create a simplified scoring system suitable for implementation in an interactive questionnaire. This scoring system was designed to provide immediate feedback regarding the presence of coronary heart disease.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Questionnaire development\u003c/h2\u003e\u003cp\u003eRegression-based weights were adapted into a simplified scoring system. Each variable response was assigned a point value, and total scores were divided into categories representing disease risk. The system was implemented in quiz-style Google Forms, allowing automatic scoring and immediate feedback.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Descriptive statistics\u003c/h2\u003e\u003cp\u003eAmong the 303 participants, about two-third were male (68.0%), while females represented 32.0%. The mean age was 54.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0 years (range: 29\u0026ndash;77). Nearly half of the sample (47.5%) reported asymptomatic chest pain, whereas 28.4% experienced non-anginal pain, 16.5% atypical angina, and only 7.6% typical angina.\u003c/p\u003e\u003cp\u003eMost participants had normal fasting blood sugar levels (85.1%), with 14.9% showing elevated levels (\u0026gt;\u0026thinsp;120 mg/dl). Resting electrocardiographic results were almost equally distributed between normal findings (49.8%) and left ventricular hypertrophy (48.8%), while ST-T abnormalities were uncommon (1.3%).\u003c/p\u003e\u003cp\u003eThe mean resting systolic blood pressure was 131.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6 mmHg (range: 94\u0026ndash;200). Categorically, 44.6% had normal systolic pressure (\u0026lt;\u0026thinsp;130 mmHg), 23.1% had stage 1 hypertension (130\u0026ndash;139 mmHg), and 32.3% had stage 2 hypertension (\u0026ge;\u0026thinsp;140 mmHg). Mean serum cholesterol was 246.7\u0026thinsp;\u0026plusmn;\u0026thinsp;51.8 mg/dl, with 16.2% \u0026lt;200 mg/dl, 32.3% between 200\u0026ndash;239 mg/dl, and 51.5% \u0026ge;240 mg/dl. Maximum heart rate averaged 149\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9 bpm (range: 71\u0026ndash;202). The mean ST-segment depression during exercise was 1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16 mm, with 32.7% showing no depression, 22.1% with \u0026lt;\u0026thinsp;1 mm, and 45.2% with \u0026ge;\u0026thinsp;1 mm depression.\u003c/p\u003e\u003cp\u003eExercise-induced angina was reported in about one-third of patients (32.7%), while the majority did not experience angina on exertion (67.3%). The slope of the peak exercise ST segment was either upsloping (46.9%) or flat (46.2%) in most patients, with only 6.9% demonstrating the clinically adverse down-sloping pattern.\u003c/p\u003e\u003cp\u003eFluoroscopy revealed no major vessels colored in 179 patients (59.1%), while one, two, and three vessels were colored in 66 (21.8%), 38 (12.5%), and 20 (6.6%) patients, respectively.\u003c/p\u003e\u003cp\u003eThalassemia-related results showed that 55.1% of participants were classified as normal, 38.9% exhibited a reversible defect, and 5.9% a fixed defect.\u003c/p\u003e\u003cp\u003eRegarding coronary heart disease (CHD) outcomes, 54.1% had no angiographic evidence of disease, while 45.9% had some degree of disease. On the ordinal severity scale, 18.2% had mild, 11.9% moderate, 11.6% significant, and 4.3% severe disease (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of categorical variables in the Cleveland Heart Disease dataset (N\u0026thinsp;=\u0026thinsp;303)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eChest pain type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTypical angina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAtypical angina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-anginal pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsymptomatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFasting blood sugar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal (\u0026lt;\u0026thinsp;120 mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElevated (\u0026gt;\u0026thinsp;120 mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eResting ECG results\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eST-T abnormality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeft ventricular hypertrophy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eResting systolic blood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage 1 hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStage 2 hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSerum cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eelevated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh levels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eST depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo ST-depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExercise-induced angina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSlope of ST-segment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpsloping (normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFlat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDown-sloping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eThalassemia status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed defect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReversible defect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eNo. of major vessels (fluoroscopy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCHD severity (0\u0026ndash;4 scale)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo disease (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMild (1 vessel)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate (2 vessels)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignificant (3 vessels)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSevere (4 vessels)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCHD presence (binary)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of continuous variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e54.44\u0026thinsp;\u0026plusmn;\u0026thinsp;9.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResting systolic blood pressure mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e131.69\u0026thinsp;\u0026plusmn;\u0026thinsp;17.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum cholesterol mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e246.69\u0026thinsp;\u0026plusmn;\u0026thinsp;51.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum heart rate achieved bpm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e149.61\u0026thinsp;\u0026plusmn;\u0026thinsp;22.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eST depression mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of major vessels (fluoroscopy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Logistic regression\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Assumption checks\u003c/h2\u003e\u003cp\u003ePrior to regression analysis, data were screened for suitability (Schober \u0026amp; Vetter, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). All the categorical predictors and dependent variables were pre-coded in SPSS v25, and no missing values were present in the dataset. the independence of observations was assumed, as each case represented a unique patient.\u003c/p\u003e\u003cp\u003eFor continuous variables (age, resting blood pressure, serum cholesterol, maximum heart rate, and ST-segment depression), the linearity of the logit was assessed using the Box-Tidwell approach. None of the interaction terms were statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the assumption of linearity was met.\u003c/p\u003e\u003cp\u003eMulticollinearity among predictors was assessed using variance inflation factors (VIF) derived from an auxiliary linear regression. All predictors showed tolerance values greater than 0.4 and VIF values below 2.5, confirming the absence of problematic multicollinearity among predictors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Binary logistic regression\u003c/h2\u003e\u003cp\u003eThe overall binary logistic regression model fit was highly satisfactory. The Omnibus test of model coefficients indicated that the logistic regression model significantly improved prediction compared to the null model (χ\u0026sup2;(18)\u0026thinsp;=\u0026thinsp;224.197, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming that the included predictors collectively explained the presence of coronary heart disease. The Cox\u0026amp; Snell R\u0026sup2; was 0.523 and a Nagelkerke R\u0026sup2; was 0.699, suggesting that the predictors explained approximately 52% to 70% of the variance in disease status. Model calibration was also acceptable, as evidenced by the Hosmer-Lemeshow test (χ\u0026sup2;(8)\u0026thinsp;=\u0026thinsp;7.093, p\u0026thinsp;=\u0026thinsp;0.527), which indicated no evidence of poor fit. The classification table demonstrated excellent discriminatory performance, with an overall accuracy of 87.5%, sensitivity of 82.7%, and specificity of 91.5%.\u003c/p\u003e\u003cp\u003eThe reference groups for categorical variables were: asymptomatic (chest pain type), reversible defect (thalassemia status), left ventricular hypertrophy (resting ECG results), down-sloping (ST segment slope), yes (exercise-induced angina), high blood sugar (fasting blood sugar), and male (sex).\u003c/p\u003e\u003cp\u003eSignificant predictors of CHD were sex, chest pain type, systolic blood pressure, number of major vessels, and thalassemia status.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSex: females had 78.3% lower odds of CHD compared to males (B=-1.526, OR\u0026thinsp;=\u0026thinsp;0.217, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eChest pain: compared to asymptomatic patients, those with typical angina had 87.9% lower odds (B= -2.11, OR\u0026thinsp;=\u0026thinsp;0.121, p\u0026thinsp;=\u0026thinsp;0.001), and those with anginal pain had 84.7% lower odds (B=-1.876, OR\u0026thinsp;=\u0026thinsp;0.153, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eResting systolic blood pressure: each 1 mmHg increase was associated with a 2.5% increase in the odds of CHD (B\u0026thinsp;=\u0026thinsp;0.024, OR\u0026thinsp;=\u0026thinsp;1.025, p\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNumber of major vessels: each additional vessel increased the odds of CHD by 271.2% (B\u0026thinsp;=\u0026thinsp;1.312, OR\u0026thinsp;=\u0026thinsp;3.712, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThalassemia status: patients with normal perfusion had 74.7% lower odds of CHD compared to those with a reversible defect (B= -1.373, OR\u0026thinsp;=\u0026thinsp;0.253, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. Ordinal logistic regression\u003c/h2\u003e\u003cp\u003eAn ordinal logistic regression model was performed to identify predictors of CHD severity, which was categorized as no disease (54.1%), mild (18.2%), moderate (11.9%), significant (11.6%), and severe (4.3%). Among the clinical variables, the number of major vessels visualized by fluoroscopy (Estimate\u0026thinsp;=\u0026thinsp;0.890, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was the strongest predictor, with higher vessel counts markedly increasing the likelihood of progressing to more severe CHD categories. Chest pain type was also significantly associated with CHD severity: compared to asymptomatic patients, those presenting with typical angina (Estimate= -1.715, p\u0026thinsp;=\u0026thinsp;0.002), atypical angina (Estimate = -1.090, p\u0026thinsp;=\u0026thinsp;0.023), and non-anginal pain (Estimate= -1.536, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significantly lower odds of being in a higher severity category. Male sex was independently associated with more severe (Estimate= -0.997, p\u0026thinsp;=\u0026thinsp;0.006). thalassemia status showed a strong relationship with severity, where a reversible perfusion defect was associate with greater odds of more severe disease compared to normal scan (Estimate=-1.310, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Resting systolic blood pressure (B\u0026thinsp;=\u0026thinsp;0.013, p\u0026thinsp;=\u0026thinsp;0.099), ST-segment depression during exercise (oldpeak, Estimate\u0026thinsp;=\u0026thinsp;0.276, p\u0026thinsp;=\u0026thinsp;0.054), and thalassemia with fixed defect (Estimate= -0.946, p\u0026thinsp;=\u0026thinsp;0.058) approached statistical significance. Other variables, including age, serum cholesterol, fasting blood sugar, maximum heart rate, resting ECG results, exercise-induced angina, and slope of the ST segment, did not show significant associations.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Development of a scoring system from ordinal logistic regression\u003c/h2\u003e\u003cp\u003eBased on the results of the ordinal logistic regression model, the significant or approached significance predictors of CHD severity were identified as number of major vessels visualized by fluoroscopy, chest pain type, sex, thalassemia status, Resting systolic blood pressure, and ST-segment depression during exercise. To translate these findings into a practical scoring system, regression coefficients were rescaled into integer points to reflect their relative contribution to CHD severity. Male sex was assigned\u0026thinsp;+\u0026thinsp;3 points, while females received 0 points. For chest pain type, asymptomatic patients had the highest risk and were assigned\u0026thinsp;+\u0026thinsp;6 points, whereas typical angina was scored 0, atypical angina\u0026thinsp;+\u0026thinsp;1, and non-anginal pain\u0026thinsp;+\u0026thinsp;1. Thalassemia status was scored as reversible defect\u0026thinsp;+\u0026thinsp;4 points (highest risk), fixed defect\u0026thinsp;+\u0026thinsp;1, and normal perfusion 0. The number of major vessels was weighted as +\u0026thinsp;3 points for each affected vessel (range 0\u0026ndash;9). Points in the scoring system were assigned by approximating each 0.3 increase in the log-odds (B coefficient) from the ordinal logistic regression as one point. In this way, stronger predictors with larger B coefficient contributed proportionally more points to the total score. For categorical variables, the reference category was always assigned zero points, and the other categories were scored relative to it. For SBP (B\u0026thinsp;=\u0026thinsp;0.013, p\u0026thinsp;=\u0026thinsp;0.099); each 1 mmHg increase adds 0.013 to the log-odds. To translate this into the scoring system (\u0026asymp;\u0026thinsp;1 point for every 0.3 log-odds), we need about 23 mmHg (0.3/0.013\u0026thinsp;\u0026asymp;\u0026thinsp;23) to each 1 point. For simplicity, it was rounded to 20 mmHg\u0026thinsp;=\u0026thinsp;1 point. For oldpeak (B\u0026thinsp;=\u0026thinsp;0.276, p\u0026thinsp;=\u0026thinsp;0.054); each 1 unit increase adds 0.276 to the log-odds. That\u0026rsquo;s already very close to 0.3, so it naturally translates to 1 point per 1 unit increase. This produced a total score range from 0 (lowest risk: female, typical angina, normal perfusion, no vessel involvement, resting systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;120 mmHg, ST-depression during exercise\u0026thinsp;\u0026lt;\u0026thinsp;1.3 mm) to 28 (highest risk: male, asymptomatic, reversible defect, three-vessel involvement, resting systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;160 mmHg, ST-depression during exercise\u0026thinsp;\u0026gt;\u0026thinsp;3.5mm) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeighted scoring system for predictors of cardiovascular risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoints\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eChest pain type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTypical angina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAtypical angina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-anginal pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsymptomatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eThalassemia status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal perfusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed defect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReversible defect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eNumber of major vessels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eResting systolic blood pressure mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140\u0026ndash;160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eST-depression during exercise mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3\u0026ndash;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.3\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter applying this scoring system to the 303 patients, the score distribution within each diagnostic was examined. Patients with no disease had a mean score of 6.64 and a median of 6.00 (range: 1\u0026ndash;22). Those with mild disease had a mean of 12.58 and a median of 12.00 (range: 4\u0026ndash;24), while moderate disease corresponded to a mean of 16.44 and a median of 17.00 (range: 9\u0026ndash;24). For significant disease, the mean score was 17.63 and the median was 17.00 (range: 8\u0026ndash;27), and patients with severe disease had the highest score, with a mean of 18.82 and a median of 19.00 (range: 10\u0026ndash;25). Using these observed distributions, cutoff points were derived to reflect the progression of CHD severity: scores of 0\u0026ndash;9 were categorized as no disease, 10\u0026ndash;13 as mild disease, 14\u0026ndash;16 as moderate disease, 17\u0026ndash;18 as significant disease, and 19\u0026ndash;28 as severe disease. These cutoffs were therefore determined empirically from the patient data, ensuring that the scoring system align with the actual distribution of disease categories in the study population (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDisease severity classification based on total score\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCutoff point\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u0026ndash;16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSignificant disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u0026ndash;28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe significant variables and categories have been converted into questions in this Google Form: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.google.com/forms/d/e/1FAIpQLSfT1-pi_WM5lSH0OwNkzn-Iu1SHajNxaMm00kivlzpPkhbTOg/viewform\u003c/span\u003e\u003cspan address=\"https://docs.google.com/forms/d/e/1FAIpQLSfT1-pi_WM5lSH0OwNkzn-Iu1SHajNxaMm00kivlzpPkhbTOg/viewform\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Each choice is assigned a weight or point value. Google Forms, by default, does not support weighted questions, so the grades or final scores are not displayed immediately after submission. However, there are add-ons for Google Forms, such as Formfacade, that can be linked to the form to provide immediate, interactive, and user-friendly results.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study illustrates how logistic regression model can be transformed into interactive questionnaires for educational use in CHD screening. The binary model allows distinction between individuals with and without disease, while the ordinal model provides additional insights into severity categories. Embedding the scoring system into Google Forms demonstrates how statistical results can be translated into practical, user-friendly formats accessible to doctors in routine settings.\u003c/p\u003e\u003cp\u003eCompared with established tools such as the Framingham Risk Score and QRISK, the proposed questionnaire differs in both scope and intention. Framingham and QRISK are validated risk calculators designed to estimate long-term cardiovascular risk. In contrast, this tool is exploratory, based on a smaller dataset, and targets short-term indicators of angiographically defined CHD. Its primary value lies in demonstrating methodological feasibility rather than replacing existing instruments.\u003c/p\u003e\u003cp\u003eKey limitations of this study should be acknowledged. First, the dataset itself is modest in size, with only 303 patients, and originates from 1980s. as a historical dataset, it reflects the medical practices and population characteristics of that era, which may differ from contemporary lifestyles, risk factor distributions, and diagnostic standards. Second, regression coefficients were simplified into integer points to allow scoring, a step that may reduce precision. Third, the model has not undergone external validation which restricts its generalizability. Finally, several predictors in the Cleveland dataset require specialized clinical testing (e.g., exercise ECG, fluoroscopy), limiting the practicality of the derived tool outside professional or research contexts.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTransforming logistic regression models into questionnaire-based scoring systems provides a practical example of how statistical results can be applied in user-friendly formats. While this tool is limited by data size, simplification, and lack of validation, it serves as a proof of concept that bridges statistical modeling with interactive screening approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Approval: Ethical approval was not required for this study as it used publicly available, de-identified data from the UCI Machine Learning Repository.\u003c/p\u003e\n\u003cp\u003eConsent for Publication: Not applicable. The study used publicly available, anonymized data and did not involve human participants.\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eNo conflict of interest\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding sources\u003c/h2\u003e\u003cp\u003eNo funding was received for this research\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNil\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCelermajer David S, Chow Clara K, Marijon E, Anstey Nicholas M, Kam W, S (2012) Cardiovascular Disease in the Developing World. JACC 60(14):1207\u0026ndash;1216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.jacc.2012.03.074\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.jacc.2012.03.074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanosi A, Steinbrunn W, Pfisterer M, Detrano R (1989) Heart Disease [Dataset]. UCI Machine Learning Repository. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24432/C52P4X\u003c/span\u003e\u003cspan address=\"10.24432/C52P4X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchober P, Vetter TR (2021) Logistic Regression in Medical Research. Anesth Analgesia 132(2):365\u0026ndash;366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1213/ane.0000000000005247\u003c/span\u003e\u003cspan address=\"https://doi:10.1213/ane.0000000000005247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Cleveland dataset, logistic regression, coronary heart disease, screening tool, questionnaire","lastPublishedDoi":"10.21203/rs.3.rs-7813237/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7813237/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a methodological approach for transforming statistical models into a practical screening tool for coronary heart disease (CHD). The Cleveland Heart Disease dataset, which includes 13 clinical and demographic variables, was analyzed using logistic regression. Two models were developed: a binary logistic regression to predict the presence or absence of CHD, and an ordinal logistic regression to examine disease severity across angiographic categories. Regression coefficient were then adapted into a simplified points-based system, suitable for implementation in quiz-style Google Forms. Each response option was assigned a weighted score, enabling automated calculation of risk upon completion of the form. The aim was to create an educational, doctor-oriented tool that highlights short-term risk factors and provides immediate feedback on both disease presence and potential severity. While this approach demonstrates how statistical modeling can be translated into an interactive and user-friendly format, limitations include the modest size and the historical nature of the Cleveland dataset, and the absence of external validation. The tool is not proposed as a validated instruments but as an example of how statistical models can be transformed into interactive and user-friendly screening formats for educational use.\u003c/p\u003e","manuscriptTitle":"Developing a Logistic Regression-Based Scoring Tool for Coronary Heart Disease Using the Cleveland Dataset: A Methodological Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 18:21:26","doi":"10.21203/rs.3.rs-7813237/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":"c3f28f22-8c01-4b24-ad24-ede7f4b5ec4f","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T18:21:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 18:21:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7813237","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7813237","identity":"rs-7813237","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}