Enhancing Patient Outcomes With Machine Learning in Healthcare Predictive Analytics

Abstract

The increasing availability of healthcare data, coupled with advances in machine learning (ML), has enabled significant progress in early disease prediction and diagnosis. This chapter explores how ML enhances early disease prediction and diagnosis by analyzing structured and unstructured healthcare data, such as EHRs, genomics, wearable sensors, and imaging. It covers key ML techniques—logistic regression, decision trees, SVMs-applied to clinical tasks. The chapter also discusses feature engineering, handling imbalanced data, and improving interpretability. Real-world use cases in detecting cancer, diabetes, cardiovascular, and neurological diseases are presented. Integration into clinical workflows is explored, showing how predictive models improve diagnostic accuracy, reduce hospital readmissions, and support preventive care. Ethical concerns such as data privacy, algorithmic bias, and the need for explainable AI are addressed. Future directions include advancements in personalized medicine, responsible AI deployment, and predictive models supporting global health initiatives.

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