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| No | Title | Models and accuracy |
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| 1 | Supervised learning techniques for analysis of neonatal data [22] | ANN, NB, SVM, and LR have been used. The highest accuracy is 89% with SVM |
| 2 | Prediction of neonatal deaths in NICUs: development and validation of machine learning models [23] | ANN, RF, CHART, SVM, and ensembles have been used. The highest accuracy is 94% with SVM |
| 3 | An artificial neural network model for neonatal disease diagnosis [24] | ANN has been used. The accuracy is 75% |
| 4 | Developing a fuzzy expert system to predict the risk of neonatal death [25] | A fuzzy model inference system has been used. The accuracy is 90% |
| 5 | Machine learning techniques for neonatal apnea prediction [26] | DT, SVM, and RF have been used. The highest accuracy is 88% with RF |
| 6 | Medical decision support using machine learning for early detection of late-onset neonatal sepsis [17] | SVM, NB, and its variants TAN and AODE, K-nearest neighbor, CART, RF, LR, and LBR. Machine learning algorithms outperformed clinicians in terms of sensitivity and specificity |
| 7 | Machine learning approach for predicting underfive mortality determinants in Ethiopia: evidence from the 2016 Ethiopian demographic and health survey [27] | RF, LR, and KNN have been used. The highest accuracy is 67.2% with RF |
| 8 | Proposed stacking model | Stacking with the highest accuracy of 97.04% |
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