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Prediction of neonatal deaths in NICUs: development and validation of machine learning models

  • Sheikhtaheri, Abbas1
  • Zarkesh, Mohammad Reza2, 2
  • Moradi, Raheleh2
  • Kermani, Farzaneh3
  • 1 Iran University of Medical Sciences, Tehran, Iran , Tehran (Iran)
  • 2 Tehran University of Medical Sciences, Tehran, Iran , Tehran (Iran)
  • 3 Semnan University of Medical Sciences, Semnan, Iran , Semnan (Iran)
Published Article
BMC Medical Informatics and Decision Making
Springer (Biomed Central Ltd.)
Publication Date
Apr 19, 2021
DOI: 10.1186/s12911-021-01497-8
Springer Nature


BackgroundPrediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians’ ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques.MethodsThis 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.Results17 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.ConclusionUsing the developed machine learning models can help physicians predict the neonatal deaths in NICUs.

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