Neonatal Mortality Prediction in NICUs: A Machine Learning Approach

Infants during the neonatal period, defined as the first four weeks after birth, exhibit the highest probability of mortality. This vulnerable stage of early life, characterized by heightened rates of mortality and neonatal diseases, underscores the susceptibility of neonatal life during this period. Consequently, delineating the mortality profile of neonates within the community is a pivotal strategy for identifying causal factors and presenting findings, thus constituting one of the most crucial approaches for enhancing neonatal health outcomes. In the field of medical science, one of the most prominent applications of machine learning is in disease diagnosis and prediction. Therefore, the aim of this research is to introduce two methodologies designed to forecast the likelihood of neonatal mortality. The first approach relies on utilizing machine learning classification algorithms, while the second approach employs convolutional neural networks (CNNs) on non-image data. To assess the developed models, metrics including Accuracy, Recall, Precision, and F-Score have been utilized. Among the methods used, the second approach, which uses CNN, performs better in predicting the probability of infant mortality during the neonatal period with 98% accuracy.