Author(s) :

Manjula Naik.

Volume/Issue :

Abstract :

Machine learning (ML) has emerged as a transformative force in healthcare, enabling more accurate and timely prediction of patient outcomes by analyzing complex and heterogeneous datasets. By leveraging diverse data sources, ML algorithms uncover subtle patterns and risk factors often missed by traditional statistical methods. This paper comprehensively explores the pivotal role of machine learning in healthcare outcome prediction, emphasizing a patient-centric approach that integrates individual patient characteristics and preferences to tailor care effectively. The discussion spans the variety of ML techniques employed, the types of clinical and non-clinical data utilized, and specific applications across medical specialties that enhance clinical decision-making and enable proactive interventions. It also critically addresses challenges such as data privacy, model transparency, bias, and integration hurdles in clinical workflows. Finally, the paper outlines future directions where advancements in explainable AI, federated learning, and patient engagement can further refine predictive models to improve healthcare delivery and outcomes.

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