Leveraging Machine Learning to Enhance Public Health Outcomes: A Comprehensive Approach to Disease Prediction, Prevention and Management

Abstract

The integration of advanced machine learning (ML) techniques into public health systems offers transformative potential for improving disease prediction, prevention, and management. With the increasing availability of large datasets and computational power, ML has emerged as a powerful tool to extract insights and make data-driven decisions in healthcare. This paper explores the application of various machine learning models, such as supervised learning, deep learning, and reinforcement learning, in addressing key challenges in public health. We discuss the impact of ML in areas such as epidemiology, chronic disease management, healthcare accessibility, and health outcomes prediction. Furthermore, we highlight the ethical considerations, data privacy concerns, and the potential for bias in ML systems when used in public health. This study also evaluates the effectiveness of novel ML techniques in reducing healthcare costs, improving patient care, and guiding public health policy development. Through case studies and a review of recent advancements, the paper presents recommendations for optimizing ML algorithms for more accurate, equitable, and efficient public health interventions.