PREDICTIVE AND PREVENTIVE MEDICINE THROUGH BIG DATA AND MACHINE INTELLIGENCE

Abstract

Predictive and preventive medicine (PPM) is transforming modern healthcare by focusing on early disease detection, risk stratification, and individualized interventions. Advances in big data analytics and machine intelligence have made it possible to integrate large-scale, heterogeneous health information—such as electronic health records (EHRs), wearable sensor data, genomic sequences, and social determinants of health—into powerful predictive models. This paper presents a comprehensive analysis of how big data and machine intelligence can enable predictive and preventive medicine. It covers foundational principles, current methods, and challenges while introducing a novel framework combining machine learning, deep learning, and health informatics infrastructure. Results from synthetic health data demonstrate the feasibility of predictive modeling for risk estimation and early detection of chronic conditions. Future directions include incorporating real-time streaming data, improved data governance, and advanced ethical frameworks.