CHILD MORTALITY PREDICTION USING MACHINE LEARNING TECHNIQUES

Abstract

Child mortality remains a significant global health challenge, particularly in developing regions, where socioeconomic, environmental, and healthcare-related factors contribute to high mortality rates. This study aims to predict child mortality using advanced machine learning techniques, leveraging large datasets comprising demographic, medical, and environmental variables. Various algorithms, including decision trees, random forests, support vector machines, and logistic regression, are employed to identify patterns and risk factors associated with child deaths. The predictive models are trained and validated to achieve high accuracy and reliability, enabling early identification of high-risk cases. By integrating machine learning with healthcare data, this approach can support policymakers, healthcare providers, and non-governmental organizations in implementing targeted interventions, optimizing resource allocation, and ultimately reducing preventable child deaths. The study demonstrates the potential of data-driven solutions in improving child health outcomes and guiding strategic decision-making in public health.