Advancing Healthcare With Predictive Analytics for Disease Diagnosis and Risk Assessment

Abstract

Predictive analytics has emerged as a transformative tool in healthcare, enabling early disease diagnosis, risk assessment, and personalized treatment. This chapter examines the application of machine learning and statistical models in clinical settings, highlighting their impact on disease prediction and prevention. Key data sources such as electronic health records (EHRs), medical imaging, genomics, and wearable devices are explored, alongside methods including deep learning, ensemble techniques, and statistical models. The chapter also addresses challenges such as data quality, bias, and privacy concerns, while discussing emerging trends like federated learning, real-time analytics, and integration with wearable devices.