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Table 6 Comparison of our results with related literature

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

First Author (Reference) Numbers of variables List of variables   Study sample Models Tool External validation Performance evaluation  
Jaskari [13] 10 Heart rate, blood pressure, GA, BW, medical scores SNAP-II and SNAPPE-II, diagnoses of the patients for BPD, NEC, ROP, information on the survival   977 VLBW infants LR, LDA, QDA, KNN, SVM, 3 different Gaussian process, RF Matlab No AUROC (RF): 0.922
F-Score (RF): 0.477
Beluzos[42] 23 Mother’s age, BW, 1-min Apgar score, 5-min Apgar score, Robson group classification, number of cesarean deliveries, fetal losses, number of previous gestation, GA, number of live births, number of normal deliveries, birth place code, childbirth care, main worker role, child-birth type (delivery), mother race/skin color, marital status, mother’s years of schooling, week of gestation (by ranges), type of pregnancy, prenatal appointments (by range), newborn presentation type, congenital malformation   698 neonates XGBoost, LR, RF Python Programming language (3.6) Yes Accuracy (RF): 93%
AUC (RF): 0.965
Rezaeian[3] 15 Maternal age, GA, number of fetus, premature rapture of membrane, maternal preeclampsia, birth type, gender of neonate, BW, birth height, birth head circumference, after birth crying, delivery room breathing, CPR in delivery room, 1th minute APGAR, 5th minute APGAR   1618 premature neonate records NN, LR MATLAB R2016a No AUC: 95.99%
Accuracy: 96.789
Sensitivity: 86.20%
Specificity: 98.37%
Cooper[12] 68 Not mention   6499 neonates Superlearning algorithm (14 regression and machine learning algorithms) SAS version 9.4, R version 3.3.0 Yes Cross-validated MSE
Excellent discrimination
(AUC development: 0.91
AUC validation: 0.87)
Good calibration in model development/ not good in model validation
Ravelli[43] 13 GA, fetal gender, use of antenatal corticosteroids, maternal age, parity, caucasian maternal ethnicity, SES, hypertension/pre-eclampsia, PROM, history of preterm birth, bleeding in the second trimester, non-cephalic presentation and level of hospital for delivery (3rd level versus non 3rd level hospital   8500 singleton very preterm infants Multiple logistic regression, Bootstrapping technique SAS version 9.2, R version 3.01 No Discrimination (AUC): 0.83
Accuracy: 65%
Calibration: Good calibration
Our study 17 BW, GA, preterm birth, SGA, prenatal care, mother disease, RDS, steroid therapy, surfactant administration, pulmonary hemorrhage, congenital malformation, NEC, sepsis, IVH, asphyxia, intubation, ventilation   Neonates admitted to NICU ANN, C5.0, SVM, Bayesian network, Ensemble IBM SPSS Modeler Yes Accuracy (SVM): 94%
Precision (RF): 98%
Specificity (RF): 94%
Sensitivity (SVM): 95%
F-score (SVM): 0.96
AUC (SVM, ensemble): 0.98
  1. The best results in literature are bold
  2. Logistic Regression (LR), Linear Discrimination Analysis (LDA), Quadratic Discrimination Analysis (QDA), K-Nearest Neighborhood (KNN), Random Forest (RF), Area Under ROC(AUROC), Mean Square Error (MSE), Area under Curve (AUC), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Support Vector Machine (SVM), Gestational Age (GA), Birth Weight (BW), Score for Neonatal Acute Physiology-II (SNAP-II), Score for Neonatal Acute Physiology with Perinatal Extension-II (SNAPPE-II), Bronchopulmonary dysplasia (BPD), Necrotizing Enter Colitis ( NEC), Retinopathy of prematurity (ROP), Cardiopulmonary Resuscitation (CPR), Socio-Economic Status (SES), Prelabour Rupture Of the Membranes (PROM), Small for Gestational Age (SGA), Respiratory Distress Syndrome (RDS), Intra Ventricular Hemorrhage (IVH)