Evaluating the predictive power of a machine learning to predict the need for neonatal resuscitation

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Hence, this study aims to predict neonatal resuscitation and affecting factors by applying machine learning. Methods: The design employed for this study was a retrospective cohort study. Data for all deliveries is gathered from the electronic health record system at a tertiary Hospital, in Bandar Abbas, Iran, between January 2020 and December 2022. Women with a single cephalic pregnancy were included, while fetal malformations were used as exclusion criteria. Twenty-eight potential factors were initially selected as a feature selection. The input data were used to train eight machine learning models. In our evaluation, we utilized accuracy, the area under the curve (AUC) of the receiver operating characteristic, precision, and recall to assess the performance. Results: During the study period, 230 (7.5%) newborns required resuscitation. The likelihood of requiring resuscitation was higher for preterm newborns, babies born to multiparous women, and those delivered via cesarean section. Conversely, mothers who received support from a doula during labor had reduced odds of neonatal resuscitation. Conditions such as preeclampsia, hypothyroidism, fetal distress, intrauterine growth retardation, and lower fetal weight were found to be linked with an increased likelihood of neonatal resuscitation. Additionally, it was observed that male newborns required more frequent resuscitation. The area under the curve (AUC) for each model turned out to be: Deep learning feed-forward (0.90), random forest classification (0.87), XGBoost classification (0.85), decision tree classification (0.85), permutation classification - knn (0.80), linear regression (0.79), light gradient-boosting (0.75), and logistic regression (0.72). All eight models showed a high accuracy ranging between 0.72-0.87. However, random forest classification performed best with AUC: 087, accuracy: 0.87, precision: 0.84, and recall: 0.90. Conclusions Employing a clinical database and multiple machine learning algorithms to assess the requirement for neonatal resuscitation shows potential benefits. Further prospective research involving intrapartum clinical attributes is necessary to enhance prediction accuracy neonatal resuscitation artificial intelligence machine learning neonatal outcome Figures Figure 1 Figure 2 Background The majority of newborns adjust to life outside the womb without any assistance. Approximately 10% may require some help, while less than 1% might need cardiac compression or medication in the delivery room [ 1 ]. Every year, approximately 1 million newborns globally lose their lives due to birth asphyxia [ 2 ]. The World Health Organization's basic newborn resuscitation guideline states that despite birth asphyxia accounting for about one-fourth of neonatal deaths, timely cardiopulmonary resuscitation during childbirth can prevent around 30% of these fatalities [ 3 ]. The data from various countries also indicates that the likelihood of mortality rises by 16% for every 30-second postponement in starting ventilation for up to six minutes and increases by 6% for every minute of delay in providing bag and mask ventilation [ 4 ]. Hence, it is evident that the initial moments following birth play a crucial role in lowering neonatal mortality rates. Research indicates that adequate resuscitation of newborns by competent, well-trained healthcare professionals, is essential in delivering suitable and sufficient intervention with the capacity to avert nearly two million neonatal deaths annually due to intrapartum-related asphyxia [ 5 ]. Usually, there is enough advance notice for a team of providers to prepare for a potential resuscitation when a newborn is born. Before birth, known risk factors can help assess the probability of requiring resuscitation [ 6 ]. Therefore, a decision-support system that accurately predicts the necessity for resuscitation would be valuable to patients and healthcare providers. This system could inform obstetricians in advance about the need for resuscitation in newborns, allowing them to carry out the required procedures immediately after birth. This would enhance the effectiveness of care provided and lower medical errors. According to our information, only one study used machine learning techniques to predict resuscitation and its risk factors in the neonatal population [ 7 ]. Hence, this study aims to predict neonatal resuscitation and affecting factors by applying machine learning. Methods The design employed for this study was a retrospective cohort study. The study's primary outcome was resuscitation, defined as a set of interventions during birth to help the newborn start breathing and establish circulation. After reviewing the literature [ 8 , 9 ], twenty eight potential factors linked to the necessity for neonatal resuscitation were initially recognized. These factors included age, place of residency, maternal education, medical insurance, nationality, history of infertility, history of neonatal death, the onset of labor, gestational age, parity, attendance of prenatal education courses, having a doula during the process in labor, method of childbirth, maternal anemia, cardiovascular disease, substances use, prolonged rupture of membrane, diabetes, maternal body mass index, preeclampsia, hypothyroidism, placenta abruption, meconium amniotic fluid, fetal distress, intrauterine growth retardation, chronic hypertension, newborn weight, and newborn sex. The abovementioned data was collected from the electronic health record system at Khaleej-e-Fars Hospital, a medical center providing specialized care in Bandar Abbas, Iran. Electronic health data for all deliveries is gathered and managed by midwives as a regular part of clinical care. The Research Committee Board of Hormozgan University of Medical Sciences approved the study. We received electronic health records for all deliveries between January 2020 and December 2022. Women with a single cephalic pregnancy were included, while fetal malformations were used as exclusion criteria. The initial step of the analysis entailed comparing no resuscitation needed with resuscitated newborns based on the listed variables. Variables with a noteworthy p-value (below 0.5) were selected as features for the machine learning method. The provided data was utilized to develop eight machine learning models. All machine learning models underwent L2 normalization for feature normalization except for tree-based models. The output of each machine-learning model fell within the range of 0 to 1. Given that neonatal resuscitation is infrequent, it was anticipated that the non-resuscitated newborns would have a significantly larger size compared to the resuscitated newborns. To prevent bias in model development and performance evaluation caused by excessively imbalanced datasets, random undersampling was conducted with a 1:10 ratio of the critical group to the non-critical group. The entire dataset was used for model development and testing, employing a five-fold cross-validation technique. This method ensured that the training and test datasets were separated appropriately, reducing any potential result distortion caused by specific divisions. Specifically, the data was split into five parts, with four used for learning and one for testing, and this five-fold testing process was conducted without any overlap. In our assessment, we employed accuracy, reflecting the ratio of correct predictions to the total predictions made. Furthermore, we evaluated the area under the curve (AUC) of the receiver operating characteristic, precision (the count of accurate predictions for a class divided by the overall predictions for that class), and recall (the count of accurate predictions for a class divided by the total actual occurrences of that class) to measure the effectiveness. For all the statistical analyses, we utilized SPSS (version 25.0, IBM Corp, Armonk, NY, United States) and Python software (version 3.7.0). Results During the study period, 2832 out of 3062 deliveries at our center (92.5%) did not require resuscitation for the newborns, while 230 newborns (7.5%) needed resuscitation. In Table 1 , you can find the maternal demographic characteristics linked to the necessity for neonatal resuscitation. None of the maternal demographic factors were found to be related to the need for neonatal resuscitation. Table 1 Demographic factors associated with the need for neonatal resuscitation Demographic characteristics No resuscitation needed (n = 2832) Resuscited newborns (n = 230) P-value Age (Years) 0.634 13–19 114 (4.0) 9 (3.9) 20–34 2037 (71.9) 159 (69.1) 35–40 576 (20.3) 50 (21.7) Above 40 105 (3.7) 12 (5.2) Residency place 0.690 Urban 2143 (75.7) 177 (77.0) Rural 689 (24.3) 53 (23.0) Education 0.057 Primary 52 (1.8) 5 (2.2) High school/Diploma 2067 (73.0) 156 (67.8) Advanced 713 (25.2) 69 (30.0) Medical Insurance 0.912 Yes 2521 (89.0) 206 (89.6) No 311 (11.0) 24 (10.4) Prenatal education course 0.717 Yes 105 (3.7) 7 (3.0) No 2727 (96.3) 223 (97.0) Nationality 0.253 Iranian 2808 (99.2) 230 (100) Non-Iranian 24 (0.8) 0 Data are presented as n (%). In Table 2 , obstetric factors are correlated with the requirement for neonatal resuscitation. The neonatal need for resuscitation was linked to gestational age, parity, the presence of a doula, and the method of childbirth. The likelihood of requiring resuscitation was higher for preterm newborns, babies born to multiparous women, and those delivered via cesarean section. Conversely, mothers who received support from a doula during labor had reduced odds of neonatal resuscitation. Table 2 Obstetric factors associated with the need for neonatal resuscitation Variables No resuscitation needed (n = 2832) Resuscited newborns (n = 230) P-value History of infertility 0.060 Yes 23 (0.8) 7 (3.0) No 2809 (99.2) 223 (97.0) History of neonatal death 0.097 Yes 12 (0.4) 3 (1.3) No 2820 (99.6) 227 (98.7) Onset of labor 0.358 Spontaneous 1436 (50.7) 126 (54.8) Induced 738 (26.1) 53 (23.0) Cesarean before the onset of labor 658 (23.2) 51 (22.2) Gestational age (week) < 0.001 Late-term (more than 41) 11 (0.4) 3 (1.3) Term (37 + 1 -41) 2481 (87.6) 127 (55.2) Preterm (24–37) 340 (12.0) 100 (43.5) Parity < 0.01 Primiparous 724 (25.6) 82 (35.7) Multiparous 2108 (74.4) 148 (64.3) Attending of doula < 0.001 Yes 632 (22.3) 26 (11.3) No 2200 (77.7) 204 (88.7) Method of childbirth < 0.001 Vaginal delivery 1673 (59.1) 92 (40.0) Operative vaginal delivery 9 (0.3) 1 (0.4) Cesarean section 1150 (40.6) 137 (59.6) Data are presented as n (%). In Table 3 , there is a representation of the correlation between maternal and newborn clinical factors and the requirement for neonatal resuscitation. Conditions such as preeclampsia, hypothyroidism, fetal distress, intrauterine growth retardation, and lower fetal weight were found to be linked with an increased likelihood of neonatal resuscitation. Additionally, it was observed that male newborns required more frequent resuscitation. Table 3 Maternal and neonatal clinical factors associated with the need for neonatal resuscitation Outcome No resuscitation needed (n = 2832) Resuscitated newborn (n = 230) P-value Maternal anemia 0.801 No 2778 (98.1) 225 (97.8) Yes 54 (1.9) 5 (2.2) Maternal cardiovascular disease 0.089 No 2813 (99.3) 226 (98.3) Yes 19 (0.7) 4 (1.7) Substances use 0.421 No 2826 (99.8) 229 (99.6) Yes 6 (0.2) 1 (0.4) Prolonged rupture of membrane 0.377 No 2793 (98.6) 225 (97.8) Yes 39 (1.4) 5 (2.2) Maternal diabetes 0.555 No 2236 (79.0) 186 (80.9) Yes 596 (21.0) 44 (19.1) Maternal body mass index (kg/m2) 0.351 Less than 18.5 124 (4.4) 8 (3.5) 18.5–24.9 1784 (63.0) 137 (59.6) 25-29.9 754 (26.6) 65 (28.3) 30 and above 170 (6.0) 20 (8.7) Preeclampsia < 0.001 No 2740 (96.8) 208 (90.4) Yes 92 (3.2) 22 (9.6) Maternal hypothyroidism 0.049 No 2481 (87.6) 191 (83.0) Yes 351 (12.4) 39 (17.0) Placenta Abruption 0.308 No 2803 (99.0) 226 (98.3) Yes 29 (1.0) 4 (1.7) Meconium amniotic fluid 0.234 No 2578 (91.0) 204 (88.7) Yes 254 (9.0) 26 (11.3) Fetal distress < 0.001 No 2711 (95.7) 202 (87.8) Yes 121 (4.3) 28 (12.2) Intrauterine growth retardation 0.030 No 2749 (97.1) 217 (94.3) Yes 83 (2.9) 13 (5.7) Chronic hypertension 0.734 No 2802 (98.9) 227 (98.7) Yes 30 (1.1) 3 (1.3) Newborn sex 0.049 Male 1447 (51.1) 131 (57.0) Female 1385 (48.9) 99 (43.0) Newborn weight (gr) < 0.001 Less than 1500 25 (0.9) 30 (13.0) 1501–2500 302 (10.7) 57 (24.8) 2501–4000 2462 (86.9) 139 (60.4) Above 4000 43 (1.5) 4 (1.7) Data are presented as n (%). As shown in Fig. 1 , the AUC for each model turned out to be: Deep learning feed-forward (0.90), random forest classification (0.87), XGBoost classification (0.85), decision tree classification (0.85), permutation classification - knn (0.80), linear regression (0.79), light gradient-boosting (0.75), and logistic regression (0.72). The machine learning models demonstrated varying performance, as indicated in Table 4 . Comparing all the diagnostic performance parameters showed that random forest classification has the best performance with AUC: 087, accuracy: 0.87, precision: 0.84, and recall: 0.90). Table 4 The performance of machine learning models. Machine learning model Accuracy AUC Precision Recall Random forest classification 0.87 0.87 0.84 0.90 Decision tree classification 0.85 0.85 0.80 0.89 XGBoost classification 0.85 0.85 0.82 0.87 Deep learning feed-forward 0.83 0.90 0.77 0.89 Permutation classification - knn 0.80 0.80 0.68 0.92 Light gradient-boosting 0.75 0.75 0.74 0.77 Logistic regression 0.72 0.72 0.75 0.70 Linear regression 0.72 0.79 0.74 0.69 AUC: area under the curve Random forest classification feature importance in predicting neonatal resuscitation is shown in Fig. 2 . The most weighted predictors identified by random forest classification were gestational age, maternal education, maternal body mass index, onset of labor, and newborn weight. Discussion Machine learning (ML) falls under the category of artificial intelligence and is experiencing quick growth within the technical domain. Due to the immense volume of structured and unstructured data (big data), ML has become essential, as traditional methods are impractical for handling such data [ 10 ]. Big data allows machine learning algorithms to discover previously unknown patterns, which in turn influences the decision-making process. Machine learning involves teaching machines to mimic human behavior. Machine learning is widely used in many different fields in today’s society. Its main applications involve classification and prediction [ 11 – 13 ]. So far, fewer machine-learning models have been released in the neonatology field. In this study, we carried out a cohort study to assess how effective machine learning models are in predicting the need for neonatal resuscitation. We applied eight different machine learning models to identify the predictors of the neonatal need for resuscitation. All eight models showed a high accuracy ranging between 0.72–0.87. However, random forest classification performed best with AUC: 087, accuracy: 0.87, precision: 0.84, and recall: 0.90. Based on our findings, 7.5% of newborns required resuscitation. The likelihood of requiring resuscitation was higher for preterm newborns, babies born to multiparous women, and those delivered via cesarean section. Conversely, mothers who received support from a doula during labor had reduced odds of neonatal resuscitation. Conditions such as preeclampsia, hypothyroidism, fetal distress, intrauterine growth retardation, and lower fetal weight were found to be linked with an increased likelihood of neonatal resuscitation. Not much prior research has delved into the predictions for neonatal resuscitation needs. Aziz et al. discovered that maternal hypertension, maternal infection, multiple pregnancies, and oligohydramnios are distinct factors that independently increase the risk of requiring positive pressure ventilation and/or endotracheal intubation [ 6 ]. In a research conducted by Afjeh et al., it was found that low birth weight, meconium-stained amniotic fluid, and chorioamnionitis are separate risk factors for needing endotracheal intubation [ 14 ]. De Almeida and colleagues discovered that multiple pregnancies, maternal hypertension, mal-presentation, cesarean section, and lower gestational age were all predictors of neonatal need for resuscitation [ 15 ]. Recently a study by Lee et al., indicated that extensive resuscitation at birth was linked to lower gestational age, lower birth weight, birth weight below the third percentile, being male, maternal hypertension, abnormal levels of amniotic fluid, lack of antenatal steroid use, being born outside the hospital, and chorioamnionitis [ 16 ]. Among all predictors defined by previous studies using both traditional statistical analysis and machine learning models, gestational age was considered the primary indicator in determining the likelihood of infant mortality and the necessity for resuscitation. Our research aligns with the majority of studies in this area, showing that both pre-term and late-term babies required resuscitation more frequently than full-term babies [ 17 , 18 ]. Lung immaturity in preterm newborns and the potential for co-complications in late-term pregnancy, such as the presence of meconium in the amniotic fluid, raises the likelihood of need for resuscitation [ 19 , 20 ]. One previous study using data mining to predict the need for neonatal resuscitation showed sensitivity results higher than 90% and specificity and accuracy results superior to 98%, which were considered satisfactory [ 7 ]. Our findings also showed a high accuracy. Our model has the potential to significantly change neonatal care by providing clinicians with accurate predictions and enabling timely interventions, which has a significant clinical impact. The application ML in multiple fields of medicine is certainly demonstrating significant potential to enhance healthcare. ML algorithms find applications in various tasks, including diagnosis and treatment, as well as drug discovery and personalized healthcare. ML's capability to examine large datasets and detect intricate patterns is resulting in improved diagnoses, enhanced treatments, and streamlined healthcare systems [ 21 – 27 ]. Our study's use of eight different machine-learning models is a key strength. However, our study has several limitations. One limitation is the retrospective design. To reduce selection bias, we made efforts to include all consecutive mothers who gave birth during the study period. Essential variables that would allow assessment of the fetus, such as biophysical profile, amniotic fluid index, and corticosteroid use before childbirth in cases of preterm births, were not included in the machine learning database. Male sex and newborn weight were associated with extensive resuscitation at birth but were not used in the predictive model because those factors cannot be determined before labor. These limitations should be considered in further studies. Conclusion The probability of needing resuscitation was greater for preterm infants, infants born to multiparous mothers, and those born through cesarean delivery. In contrast, mothers assisted by a doula during labor experienced lower chances of needing neonatal resuscitation. Conditions like preeclampsia, hypothyroidism, fetal distress, intrauterine growth restriction, and reduced fetal weight were associated with a higher chance of neonatal resuscitation. Moreover, it was noted that male infants needed resuscitation more often. Employing a clinical database and multiple machine learning algorithms to assess the requirement for neonatal resuscitation shows potential benefits. If confirmed in other cohorts, a prediction system that effectively forecasts the need for resuscitation could offer significant benefits to both patients and healthcare professionals. Further prospective research involving intrapartum clinical attributes is necessary to enhance prediction accuracy. Abbreviations BMI: Body mass index XGBoost: Extreme gradient boost KNN: K-nearest neighbors Declarations Ethics approval and consent to participate This study complied with the Declaration of Helsinki and was performed according to ethics committee approval. The Ethics and Research Committee of the Hormozgan University of Medical Sciences approved the study (IR.HUMS.REC.1403.226). The records of all patients who provided informed consent for using their data for research purposes were analyzed. The legal guardians of individuals under the age of eighteen provided written informed consent. Statistical analysis was performed with patient anonymity following ethics committee regulations. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding Hormozgan University of Medical Sciences. Author contributions M.B. and N.R. wrote the proposal. F.M. contributed significantly to data collection. M.V. and V.M. analyzed and interpreted the findings. F.D. was responsible for the manuscript's writing and editing. F.A. assessed the manuscript's scientific content critically. All authors read and approved the final manuscript for submission. 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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-8455786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588023088,"identity":"6c64fce7-8f43-4b16-8700-c74111b57589","order_by":0,"name":"Mojdeh Banaei","email":"","orcid":"","institution":"Hormozgan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mojdeh","middleName":"","lastName":"Banaei","suffix":""},{"id":588023089,"identity":"deed0508-b977-482a-b0c5-c2c185785400","order_by":1,"name":"Nasibeh Roozbeh","email":"","orcid":"","institution":"Hormozgan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nasibeh","middleName":"","lastName":"Roozbeh","suffix":""},{"id":588023090,"identity":"e75582c7-cd98-440a-82fb-4dbee707b138","order_by":2,"name":"Fatemeh Abdi","email":"","orcid":"","institution":"Iran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Abdi","suffix":""},{"id":588023091,"identity":"36282d9a-d6d9-4d82-a058-5880cf8bcb36","order_by":3,"name":"Fatemeh Darsareh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBAC9gYgwQNhszF8YGBIIKiF5wCSFsYZJGth5iFKC/vpxA9vau7Z888+fOyxbZtdHj97A+OHjzl4tPDkbpacc6w4cca5tHTj3LbkYsmeA8ySM7fh1mLPkLtBmoctIYHhDI+ZdG4bc+KGGwlszLx4tPDwv938m+dfgr38Gf5v0pZt9URokcjdJs3blsC44QwPmzRj22FitLzdZjm3LyFx4xk2M8mec8cTZ/YcbMbrFx7+3M033nxLsJc7w/xM4kdZdWI/e/PBDx/xaEEFjGxgsoFY9SDwhxTFo2AUjIJRMFIAALTpURiTGy40AAAAAElFTkSuQmCC","orcid":"","institution":"Hormozgan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Darsareh","suffix":""},{"id":588023092,"identity":"f10255a6-c7a9-4bce-b572-140618e8f3b4","order_by":4,"name":"Vahid Mehrnoush","email":"","orcid":"","institution":"Hormozgan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vahid","middleName":"","lastName":"Mehrnoush","suffix":""},{"id":588023093,"identity":"edde3948-c114-4cf1-9c63-0c3a9373ca47","order_by":5,"name":"Farideh Montazeri","email":"","orcid":"","institution":"Hormozgan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Farideh","middleName":"","lastName":"Montazeri","suffix":""},{"id":588023094,"identity":"f491da1a-2952-45e0-975d-7f0881bbd804","order_by":6,"name":"Mohammadsadegh Vahidi Farashah","email":"","orcid":"","institution":"Hormozgan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammadsadegh","middleName":"Vahidi","lastName":"Farashah","suffix":""}],"badges":[],"createdAt":"2025-12-26 13:53:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8455786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8455786/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102746313,"identity":"d19f0982-7b5f-43ae-98b3-c19b1695fda3","added_by":"auto","created_at":"2026-02-16 08:56:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAUC Chart of Algorithms\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNCPR: Neonatal cardiopulmonary resuscitation\u003c/p\u003e\n\u003cp\u003eLGB: Light gradient-boosting\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8455786/v1/9abb1f0ed2aea8e425a8e275.png"},{"id":102746282,"identity":"cdef516c-ea04-4b6d-8905-48e253a217bc","added_by":"auto","created_at":"2026-02-16 08:56:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRandom Forest Classification Feature Importance\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8455786/v1/87aad063e5eaf69bdc6f998a.png"},{"id":102750652,"identity":"45881294-af23-40b7-9120-1f1d8819b29e","added_by":"auto","created_at":"2026-02-16 09:21:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1047793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8455786/v1/1be22625-4430-4990-a31f-ed908a7fa47c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the predictive power of a machine learning to predict the need for neonatal resuscitation","fulltext":[{"header":"Background","content":"\u003cp\u003eThe majority of newborns adjust to life outside the womb without any assistance. Approximately 10% may require some help, while less than 1% might need cardiac compression or medication in the delivery room [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Every year, approximately 1\u0026nbsp;million newborns globally lose their lives due to birth asphyxia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The World Health Organization's basic newborn resuscitation guideline states that despite birth asphyxia accounting for about one-fourth of neonatal deaths, timely cardiopulmonary resuscitation during childbirth can prevent around 30% of these fatalities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The data from various countries also indicates that the likelihood of mortality rises by 16% for every 30-second postponement in starting ventilation for up to six minutes and increases by 6% for every minute of delay in providing bag and mask ventilation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Hence, it is evident that the initial moments following birth play a crucial role in lowering neonatal mortality rates. Research indicates that adequate resuscitation of newborns by competent, well-trained healthcare professionals, is essential in delivering suitable and sufficient intervention with the capacity to avert nearly two million neonatal deaths annually due to intrapartum-related asphyxia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsually, there is enough advance notice for a team of providers to prepare for a potential resuscitation when a newborn is born. Before birth, known risk factors can help assess the probability of requiring resuscitation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, a decision-support system that accurately predicts the necessity for resuscitation would be valuable to patients and healthcare providers. This system could inform obstetricians in advance about the need for resuscitation in newborns, allowing them to carry out the required procedures immediately after birth. This would enhance the effectiveness of care provided and lower medical errors. According to our information, only one study used machine learning techniques to predict resuscitation and its risk factors in the neonatal population [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hence, this study aims to predict neonatal resuscitation and affecting factors by applying machine learning.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe design employed for this study was a retrospective cohort study. The study's primary outcome was resuscitation, defined as a set of interventions during birth to help the newborn start breathing and establish circulation. After reviewing the literature [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], twenty eight potential factors linked to the necessity for neonatal resuscitation were initially recognized. These factors included age, place of residency, maternal education, medical insurance, nationality, history of infertility, history of neonatal death, the onset of labor, gestational age, parity, attendance of prenatal education courses, having a doula during the process in labor, method of childbirth, maternal anemia, cardiovascular disease, substances use, prolonged rupture of membrane, diabetes, maternal body mass index, preeclampsia, hypothyroidism, placenta abruption, meconium amniotic fluid, fetal distress, intrauterine growth retardation, chronic hypertension, newborn weight, and newborn sex. The abovementioned data was collected from the electronic health record system at Khaleej-e-Fars Hospital, a medical center providing specialized care in Bandar Abbas, Iran. Electronic health data for all deliveries is gathered and managed by midwives as a regular part of clinical care. The Research Committee Board of Hormozgan University of Medical Sciences approved the study. We received electronic health records for all deliveries between January 2020 and December 2022. Women with a single cephalic pregnancy were included, while fetal malformations were used as exclusion criteria.\u003c/p\u003e \u003cp\u003eThe initial step of the analysis entailed comparing no resuscitation needed with resuscitated newborns based on the listed variables. Variables with a noteworthy p-value (below 0.5) were selected as features for the machine learning method. The provided data was utilized to develop eight machine learning models. All machine learning models underwent L2 normalization for feature normalization except for tree-based models. The output of each machine-learning model fell within the range of 0 to 1.\u003c/p\u003e \u003cp\u003eGiven that neonatal resuscitation is infrequent, it was anticipated that the non-resuscitated newborns would have a significantly larger size compared to the resuscitated newborns. To prevent bias in model development and performance evaluation caused by excessively imbalanced datasets, random undersampling was conducted with a 1:10 ratio of the critical group to the non-critical group. The entire dataset was used for model development and testing, employing a five-fold cross-validation technique. This method ensured that the training and test datasets were separated appropriately, reducing any potential result distortion caused by specific divisions. Specifically, the data was split into five parts, with four used for learning and one for testing, and this five-fold testing process was conducted without any overlap.\u003c/p\u003e \u003cp\u003eIn our assessment, we employed accuracy, reflecting the ratio of correct predictions to the total predictions made. Furthermore, we evaluated the area under the curve (AUC) of the receiver operating characteristic, precision (the count of accurate predictions for a class divided by the overall predictions for that class), and recall (the count of accurate predictions for a class divided by the total actual occurrences of that class) to measure the effectiveness. For all the statistical analyses, we utilized SPSS (version 25.0, IBM Corp, Armonk, NY, United States) and Python software (version 3.7.0).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the study period, 2832 out of 3062 deliveries at our center (92.5%) did not require resuscitation for the newborns, while 230 newborns (7.5%) needed resuscitation. In Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, you can find the maternal demographic characteristics linked to the necessity for neonatal resuscitation. None of the maternal demographic factors were found to be related to the need for neonatal resuscitation.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic factors associated with the need for neonatal resuscitation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo resuscitation needed (n\u0026thinsp;=\u0026thinsp;2832)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResuscited newborns\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;230)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2037 (71.9)\u003c/p\u003e\n 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place\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2143 (75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177 (77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e689 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school/Diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2067 (73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156 (67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdvanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e713 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2521 (89.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrenatal education course\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2727 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIranian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2808 (99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Iranian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as n (%).\u003c/p\u003e\n\u003cp\u003eIn Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, obstetric factors are correlated with the requirement for neonatal resuscitation. The neonatal need for resuscitation was linked to gestational age, parity, the presence of a doula, and the method of childbirth. The likelihood of requiring resuscitation was higher for preterm newborns, babies born to multiparous women, and those delivered via cesarean section. Conversely, mothers who received support from a doula during labor had reduced odds of neonatal resuscitation.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eObstetric factors associated with the need for neonatal resuscitation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo resuscitation needed (n\u0026thinsp;=\u0026thinsp;2832)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResuscited newborns (n\u0026thinsp;=\u0026thinsp;230)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2809 (99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of neonatal death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2820 (99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e227 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnset of labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpontaneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1436 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInduced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e738 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCesarean before the onset of labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e658 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGestational age (week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLate-term (more than 41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerm (37\u003csup\u003e+\u0026thinsp;1\u003c/sup\u003e-41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2481 (87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreterm (24\u0026ndash;37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e724 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2108 (74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttending of doula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e632 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2200 (77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204 (88.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethod of childbirth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1673 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOperative vaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCesarean section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1150 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137 (59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as n (%).\u003c/p\u003e\n\u003cp\u003eIn Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, there is a representation of the correlation between maternal and newborn clinical factors and the requirement for neonatal resuscitation. Conditions such as preeclampsia, hypothyroidism, fetal distress, intrauterine growth retardation, and lower fetal weight were found to be linked with an increased likelihood of neonatal resuscitation. Additionally, it was observed that male newborns required more frequent resuscitation.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMaternal and neonatal clinical factors associated with the need for neonatal resuscitation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo resuscitation needed\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2832)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResuscitated newborn\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;230)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2778 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal cardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2813 (99.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226 (98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstances use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2826 (99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229 (99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProlonged rupture of membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2793 (98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2236 (79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186 (80.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e596 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal body mass index (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1784 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137 (59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e754 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreeclampsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2740 (96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208 (90.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal hypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2481 (87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191 (83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlacenta Abruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2803 (99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226 (98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeconium amniotic fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2578 (91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204 (88.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e254 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFetal distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2711 (95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntrauterine growth retardation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2749 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2802 (98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e227 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNewborn sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1447 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e131 (57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1385 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNewborn weight (gr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than 1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1501\u0026ndash;2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e302 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2501\u0026ndash;4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2462 (86.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove 4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as n (%).\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the AUC for each model turned out to be: Deep learning feed-forward (0.90), random forest classification (0.87), XGBoost classification (0.85), decision tree classification (0.85), permutation classification - knn (0.80), linear regression (0.79), light gradient-boosting (0.75), and logistic regression (0.72).\u003c/p\u003e\n\u003cp\u003eThe machine learning models demonstrated varying performance, as indicated in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Comparing all the diagnostic performance parameters showed that random forest classification has the best performance with AUC: 087, accuracy: 0.87, precision: 0.84, and recall: 0.90).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe performance of machine learning models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMachine learning model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecall\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom forest classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecision tree classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXGBoost classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep learning feed-forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermutation classification - knn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLight gradient-boosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogistic regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAUC: area under the curve\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eRandom forest classification feature importance in predicting neonatal resuscitation is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The most weighted predictors identified by random forest classification were gestational age, maternal education, maternal body mass index, onset of labor, and newborn weight.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMachine learning (ML) falls under the category of artificial intelligence and is experiencing quick growth within the technical domain. Due to the immense volume of structured and unstructured data (big data), ML has become essential, as traditional methods are impractical for handling such data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Big data allows machine learning algorithms to discover previously unknown patterns, which in turn influences the decision-making process. Machine learning involves teaching machines to mimic human behavior. Machine learning is widely used in many different fields in today\u0026rsquo;s society. Its main applications involve classification and prediction [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. So far, fewer machine-learning models have been released in the neonatology field. In this study, we carried out a cohort study to assess how effective machine learning models are in predicting the need for neonatal resuscitation. We applied eight different machine learning models to identify the predictors of the neonatal need for resuscitation. All eight models showed a high accuracy ranging between 0.72\u0026ndash;0.87. However, random forest classification performed best with AUC: 087, accuracy: 0.87, precision: 0.84, and recall: 0.90.\u003c/p\u003e \u003cp\u003eBased on our findings, 7.5% of newborns required resuscitation. The likelihood of requiring resuscitation was higher for preterm newborns, babies born to multiparous women, and those delivered via cesarean section. Conversely, mothers who received support from a doula during labor had reduced odds of neonatal resuscitation. Conditions such as preeclampsia, hypothyroidism, fetal distress, intrauterine growth retardation, and lower fetal weight were found to be linked with an increased likelihood of neonatal resuscitation. Not much prior research has delved into the predictions for neonatal resuscitation needs. Aziz et al. discovered that maternal hypertension, maternal infection, multiple pregnancies, and oligohydramnios are distinct factors that independently increase the risk of requiring positive pressure ventilation and/or endotracheal intubation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In a research conducted by Afjeh et al., it was found that low birth weight, meconium-stained amniotic fluid, and chorioamnionitis are separate risk factors for needing endotracheal intubation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. De Almeida and colleagues discovered that multiple pregnancies, maternal hypertension, mal-presentation, cesarean section, and lower gestational age were all predictors of neonatal need for resuscitation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recently a study by Lee et al., indicated that extensive resuscitation at birth was linked to lower gestational age, lower birth weight, birth weight below the third percentile, being male, maternal hypertension, abnormal levels of amniotic fluid, lack of antenatal steroid use, being born outside the hospital, and chorioamnionitis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong all predictors defined by previous studies using both traditional statistical analysis and machine learning models, gestational age was considered the primary indicator in determining the likelihood of infant mortality and the necessity for resuscitation. Our research aligns with the majority of studies in this area, showing that both pre-term and late-term babies required resuscitation more frequently than full-term babies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Lung immaturity in preterm newborns and the potential for co-complications in late-term pregnancy, such as the presence of meconium in the amniotic fluid, raises the likelihood of need for resuscitation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. One previous study using data mining to predict the need for neonatal resuscitation showed sensitivity results higher than 90% and specificity and accuracy results superior to 98%, which were considered satisfactory [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Our findings also showed a high accuracy. Our model has the potential to significantly change neonatal care by providing clinicians with accurate predictions and enabling timely interventions, which has a significant clinical impact.\u003c/p\u003e \u003cp\u003eThe application ML in multiple fields of medicine is certainly demonstrating significant potential to enhance healthcare. ML algorithms find applications in various tasks, including diagnosis and treatment, as well as drug discovery and personalized healthcare. ML's capability to examine large datasets and detect intricate patterns is resulting in improved diagnoses, enhanced treatments, and streamlined healthcare systems [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study's use of eight different machine-learning models is a key strength. However, our study has several limitations. One limitation is the retrospective design. To reduce selection bias, we made efforts to include all consecutive mothers who gave birth during the study period. Essential variables that would allow assessment of the fetus, such as biophysical profile, amniotic fluid index, and corticosteroid use before childbirth in cases of preterm births, were not included in the machine learning database. Male sex and newborn weight were associated with extensive resuscitation at birth but were not used in the predictive model because those factors cannot be determined before labor. These limitations should be considered in further studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe probability of needing resuscitation was greater for preterm infants, infants born to multiparous mothers, and those born through cesarean delivery. In contrast, mothers assisted by a doula during labor experienced lower chances of needing neonatal resuscitation. Conditions like preeclampsia, hypothyroidism, fetal distress, intrauterine growth restriction, and reduced fetal weight were associated with a higher chance of neonatal resuscitation. Moreover, it was noted that male infants needed resuscitation more often. Employing a clinical database and multiple machine learning algorithms to assess the requirement for neonatal resuscitation shows potential benefits. If confirmed in other cohorts, a prediction system that effectively forecasts the need for resuscitation could offer significant benefits to both patients and healthcare professionals. Further prospective research involving intrapartum clinical attributes is necessary to enhance prediction accuracy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eXGBoost: Extreme gradient boost\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKNN: K-nearest neighbors\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with the Declaration of Helsinki and was performed according to ethics committee approval. The Ethics and Research Committee of the Hormozgan University of Medical Sciences approved the study (IR.HUMS.REC.1403.226). The records of all patients who provided informed consent for using their data for research purposes were analyzed. The legal guardians of individuals under the age of eighteen provided written informed consent. Statistical analysis was performed with patient anonymity following ethics committee regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHormozgan University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.B. and N.R. wrote the proposal. F.M. contributed significantly to data collection. M.V. and V.M. analyzed and interpreted the findings. F.D. was responsible for the manuscript\u0026apos;s writing and editing. F.A. assessed the manuscript\u0026apos;s scientific content critically. All authors read and approved the final manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the authors acknowledged Hormozgan University of Medical Sciences.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWyckoff MH, Aziz K, Escobedo MB, Kapadia VS, Kattwinkel J, Perlman JM, Simon WM, Weiner GM, Zaichkin JG. Part 13: neonatal resuscitation: 2015 American Heart Association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation. 2015;132(18 Suppl 2):S543\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eSinghal N, Lockyer J, Fidler H, et al. 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BJA Educ. 2021;21(12):479-485. doi:10.1016/j.bjae.2021.07.008\u003c/li\u003e\n\u003cli\u003eJordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255-260. doi:10.1126/science.aaa8415\u003c/li\u003e\n\u003cli\u003eTaeidi E, Ranjbar A, Montazeri F, Mehrnoush V, Darsareh F. Machine Learning-Based Approach to Predict Intrauterine Growth Restriction. Cureus. 2023;15(7):e41448. Published 2023 Jul 6. doi:10.7759/cureus.41448\u003c/li\u003e\n\u003cli\u003eBanaei M, Roozbeh N, Darsareh F, Mehrnoush V, Farashah MSV, Montazeri F. Utilizing machine learning to predict the risk factors of episiotomy in parturient women. AJOG Glob Rep. 2024 Nov 13;5(1):100420. doi: 10.1016/j.xagr.2024.100420.\u003c/li\u003e\n\u003cli\u003eRanjbar A, Taeidi E, Mehrnoush V, Roozbeh N, Darsareh F. Machine learning models for predicting pre-eclampsia: a systematic review protocol. BMJ Open. 2023 Sep 11;13(9):e074705. doi: 10.1136/bmjopen-2023-074705. \u003c/li\u003e\n\u003cli\u003eAfjeh SA, Sabzehei MK, Esmaili F. Neonatal resuscitation in the delivery room from a tertiary level hospital: risk factors and outcome. Iran J Pediatr. 2013;23(6):675\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003ede Almeida MF, Guinsburg R, da Costa JO, Anchieta LM, Freire LM, Junior DC. Resuscitative procedures at birth in late preterm infants. J Perinatol. 2007;27(12):761\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eLee J, Lee JH. A clinical scoring system to predict the need for extensive resuscitation at birth in very low birth weight infants. BMC Pediatr. 2019;19(1):197. Published 2019 Jun 14. doi:10.1186/s12887-019-1573-9\u003c/li\u003e\n\u003cli\u003eCavolo A, Dierckx de Casterl\u0026eacute; B, Naulaers G, Gastmans C. Neonatologists\u0026apos; Resuscitation Decisions at Birth for Extremely Premature Infants. A Belgian Qualitative Study. Front Pediatr. 2022;10:852073. Published 2022 Mar 24. doi:10.3389/fped.2022.852073\u003c/li\u003e\n\u003cli\u003eSuman V, Luther EE. Preterm Labor. [Updated 2023 Aug 8]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK536939/\u003c/li\u003e\n\u003cli\u003eRanjbar A, Mehrnoush V, Darsareh F, Pariafsay F, Shirzadfardjahromi M, Shekari M. The Incidence and Outcomes of Late-Term Pregnancy. Cureus. 2023;15(1):e33550. Published 2023 Jan 9. doi:10.7759/cureus.33550\u003c/li\u003e\n\u003cli\u003eShekari M, Jahromi MS, Ranjbar A, Mehrnoush V, Darsareh F, Roozbeh N. The incidence and risk factors of meconium amniotic fluid in singleton pregnancies: an experience of a tertiary hospital in Iran. BMC Pregnancy Childbirth. 2022;22(1):930. Published 2022 Dec 12. doi:10.1186/s12884-022-05285-8\u003c/li\u003e\n\u003cli\u003eBoujarzadeh B, Ranjbar A, Banihashemi F, Mehrnoush V, Darsareh F, Saffari M. Machine learning approach to predict postpartum haemorrhage: a systematic review protocol. BMJ Open. 2023 Jan 19;13(1):e067661. doi: 10.1136/bmjopen-2022-067661.\u003c/li\u003e\n\u003cli\u003eMalakooti N, Mehrnoush V, Abdi F, Farashah MSV, Darsareh F. Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission. Sci Rep. 2025 Jul 1;15(1):20914. doi: 10.1038/s41598-025-06651-0.\u003c/li\u003e\n\u003cli\u003eMehrnoush, V, Darsareh, F, Shabana,W, Shahrour W. Prediction of renal Cancer recurrence using artificial intelligence: A systematic review. Canc Therapy Oncol. Int. J. 2025;28 (2), 556233. doi:10.19080/CTOIJ.2025.28.556233.\u003c/li\u003e\n\u003cli\u003eRoozbeh N, Montazeri F, Farashah MV, Mehrnoush V, Darsareh F. Proposing a machine learning-based model for predicting nonreassuring fetal heart. Sci Rep. 2025 Mar 6;15(1):7812. doi: 10.1038/s41598-025-92810-2.\u003c/li\u003e\n\u003cli\u003eSafarzadeh S, Ardabili NS, Farashah MV, Roozbeh N, Darsareh F. Predicting mother and newborn skin-to-skin contact using a machine learning approach. BMC Pregnancy Childbirth. 2025 Feb 18;25(1):182. doi: 10.1186/s12884-025-07313-9.\u003c/li\u003e\n\u003cli\u003eVatankhah Tarbebar M, Mohammadi M, Mehrnoush V, Darsareh F. Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol. BMJ Open. 2025 Oct 28;15(10):e108019. doi: 10.1136/bmjopen-2025-108019.\u003c/li\u003e\n\u003cli\u003eAbdi F, Roozbeh N, Darsareh F, Mehrnoush V, Vahidi Farashah MS, Montazeri F. Developing a prognostic model for predicting preterm birth using a machine learning algorithm. BMC Pregnancy Childbirth. 2025 Sep 30;25(1):974. doi: 10.1186/s12884-025-08136-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"maternal-health-neonatology-and-perinatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mhnp","sideBox":"Learn more about [Maternal Health, Neonatology and Perinatology](http://mhnpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mhnp/default.aspx","title":"Maternal Health, Neonatology and Perinatology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"neonatal resuscitation, artificial intelligence, machine learning, neonatal outcome","lastPublishedDoi":"10.21203/rs.3.rs-8455786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8455786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDeveloping a system that accurately predicts the necessity for resuscitation would be valuable to patients and healthcare providers. Hence, this study aims to predict neonatal resuscitation and affecting factors by applying machine learning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe design employed for this study was a retrospective cohort study. Data for all deliveries is gathered from the electronic health record system at a tertiary Hospital, in Bandar Abbas, Iran, between January 2020 and December 2022. Women with a single cephalic pregnancy were included, while fetal malformations were used as exclusion criteria. Twenty-eight potential factors were initially selected as a feature selection. The input data were used to train eight machine learning models. In our evaluation, we utilized accuracy, the area under the curve (AUC) of the receiver operating characteristic, precision, and recall to assess the performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e During the study period, 230 (7.5%) newborns required resuscitation. The likelihood of requiring resuscitation was higher for preterm newborns, babies born to multiparous women, and those delivered via cesarean section. Conversely, mothers who received support from a doula during labor had reduced odds of neonatal resuscitation. Conditions such as preeclampsia, hypothyroidism, fetal distress, intrauterine growth retardation, and lower fetal weight were found to be linked with an increased likelihood of neonatal resuscitation. Additionally, it was observed that male newborns required more frequent resuscitation. The area under the curve (AUC) for each model turned out to be: Deep learning feed-forward (0.90), random forest classification (0.87), XGBoost classification (0.85), decision tree classification (0.85), permutation classification - knn (0.80), \u0026nbsp;linear regression (0.79), light gradient-boosting (0.75), and logistic regression (0.72). All eight models showed a high accuracy ranging between 0.72-0.87. However, random forest classification performed best with AUC: 087, accuracy: 0.87, precision: 0.84, and recall: 0.90.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e Employing a clinical database and multiple machine learning algorithms to assess the requirement for neonatal resuscitation shows potential benefits. Further prospective research involving intrapartum clinical attributes is necessary to enhance prediction accuracy\u003c/p\u003e","manuscriptTitle":"Evaluating the predictive power of a machine learning to predict the need for neonatal resuscitation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 11:05:48","doi":"10.21203/rs.3.rs-8455786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-21T16:16:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T14:57:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9959232517083564625897780131779225304","date":"2026-03-23T18:44:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231387090469396714047959375527743645165","date":"2026-03-22T19:21:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63345943755748329406548260134091277526","date":"2026-02-09T06:45:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T05:55:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-31T12:45:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T10:19:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Maternal Health, Neonatology and Perinatology","date":"2025-12-26T13:45:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"maternal-health-neonatology-and-perinatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mhnp","sideBox":"Learn more about [Maternal Health, Neonatology and Perinatology](http://mhnpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mhnp/default.aspx","title":"Maternal Health, Neonatology and Perinatology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53a98089-d3f8-45f2-bc51-076e7058022b","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T11:05:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 11:05:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8455786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8455786","identity":"rs-8455786","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}