Predictive modeling has become an essential methodological framework in modern healthcare, supporting clinical decision-making across diagnosis, prognosis, and treatment planning. Advances in artificial intelligence and machine learning have expanded the scope of predictive models, enabling the integration of heterogeneous clinical, biological, imaging, and nonclinical data. This article provides a comprehensive and methodologically grounded review of predictive modeling in healthcare, emphasizing its theoretical foundations, modeling strategies, validation practices, and real-world clinical applications. Drawing on established and recent literature, the manuscript synthesizes developments in predictive modeling for cardiovascular disease risk assessment, cancer therapeutic response prediction, surgical outcome estimation, and risk stratification using physiological signals. Particular attention is given to the distinction between diagnostic and prognostic modeling, the role of nonclinical features, and the implications of model interpretability and generalizability. The Methods section outlines common data preprocessing pipelines, feature selection strategies, and modeling techniques, including traditional statistical models and machine learning approaches. The Results section synthesizes reported performance trends and comparative findings from prior studies, while the Discussion critically examines methodological limitations, ethical considerations, and future research directions. Overall, this work aims to serve as a structured reference for researchers and clinicians seeking to design, evaluate, or interpret predictive models in healthcare, while highlighting unresolved challenges related to data quality, bias, and clinical integration.

Predictive modeling, healthcare analytics, machine learning, clinical decision support, risk stratification, artificial intelligence

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