MACHINE LEARNING-DRIVEN PREDICTIVE ANALYTICS FOR CHOLERA OUT-BREAKS FORECASTING AND RESOURCE OPTIMIZATION IN ZAMBIA’S HEALTH SECTOR

Abstract

Healthcare systems worldwide are increasingly leveraging data-driven strategies to enhance decision-making, optimize resource allocation, and improve patient outcomes. This research explores the application of machine learning-driven predictive analytics for Cholera outbreaks forecasting and resource optimization in Zambia’s healthcare sector. The study focuses on communicable diseases, particularly cholera, which remains a significant public health threat due to recurring outbreaks. The research employs supervised learning algorithms, including Random Forest and Gradient Boosting, for cholera outbreak prediction and unsupervised learning techniques like K-Means for resource utilization analysis. Data will be sourced from historical health records, real-time hospital data, and external variables such as weather patterns, sanitation conditions, and population density. The model’s performance will be evaluated using metrics like accuracy, precision, recall, and F1-score to ensure reliability and effectiveness. By integrating predictive analytics into Zambia’s healthcare system, this study aims to facilitate proactive decision-making, enabling healthcare administrators to anticipate cholera outbreaks and allocate resources efficiently. The findings will contribute to evidence-based healthcare management, aligning with global best practices while addressing Zambia’s unique challenges. Ultimately, the project seeks to establish a scalable and sustainable predictive analytics framework to strengthen epidemic preparedness, enhance health system resilience, and improve patient care.