Predictive Modeling of Neonatal Mortality Trends Using Machine Learning: A Cross-sectional Data Science Approach - Pal, Kaushika; K Pal, Jitendra; Trivedi, Sejal R.; Sharma, Neha; P Patavardhan, Prashant; Goswami, Sarbojeet (Journal of Neonatal Surgery, 2025)
In this paper, we propose a machine-learning framework to estimate neonatal mortality rates from cross-sectional global health data. The authors combine cause-specific neonatal death rates and aggregate health statistics from the World Health Organization (WHO) to implement regression models for predicting the total neonatal mortality by constructing a merged dataset spanning multiple countries and years. Univariate analysis shows significant associations between causes such as prematurity, infection and birth trauma with overall mortality. We use multiple machine learning techniques like Linear Regression, Random Forest Regressor, Support Vector Regression, and Gradient Boosting to predict the inferential time of the given queries. Random Forest has the best predictive accuracy among them (R² = 0.990; RMSE = 1.24). These findings validate the feasibility of using machine learning for accurate, data-driven predictions of neonatal mortality to enhance public health policy decisions in relation to maternal, perinatal and neonatal care.