Risk Prediction Through Deep Learning Classifiers for a Child Health Protection Decision Support System

Abstract

This research presents the development and implementation of a Machine Learning  Decision Support System (ML-DSS) aimed at enhancing child health protection in Zambia. The system utilizes a predictive framework based on a star-schema database architecture, which includes a fact table containing child-level data linked to various health and educational indicators. Specifically, the ML-DSS focuses on binary classification tasks to assess school dropout risks and stunting risks among children, employing deep learning techniques facilitated by TensorFlow. Key results highlight the model’s performance metrics, demonstrating its potential to inform early interventions in child health and education. The research identifies critical factors influencing dropout rates and stunting, emphasizing the significance of nutrition and school attendance. Despite limitations, including the absence of detailed household financial data, the model provides a robust tool for NGOs to enhance their programming and improve child health outcomes.