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Application of machine learning techniques can improve physicians\u2019 ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Using the developed machine learning models can help physicians predict the neonatal deaths in NICUs.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-021-01497-8","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T16:03:14Z","timestamp":1618848194000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Prediction of neonatal deaths in NICUs: development and validation of machine learning models"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6879-5415","authenticated-orcid":false,"given":"Abbas","family":"Sheikhtaheri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7210-6803","authenticated-orcid":false,"given":"Mohammad Reza","family":"Zarkesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4841-5665","authenticated-orcid":false,"given":"Raheleh","family":"Moradi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6013-2940","authenticated-orcid":false,"given":"Farzaneh","family":"Kermani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"issue":"3","key":"1497_CR1","first-page":"70","volume":"10","author":"P Karimi","year":"2019","unstructured":"Karimi P, Mahmudi L, Azami M, Badfar G. 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The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and research regulations of the country. Considering retrospective nature of the study, Informed consent was waived by the ethics committee of Iran University of Medical Sciences.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and informed consent"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The Authors declare that there is no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"131"}}