A REVIEW OF MACHINE LEARNING ALGORITHMS IN HEALTHCARE ANALYTICS

Abstract

The integration of machine learning (ML) into healthcare analytics has revolutionized the way medical data is processed, interpreted, and utilized for clinical decision-making. With the exponential growth of electronic health records, imaging data, and real-time patient monitoring, machine learning techniques provide robust tools for prediction, diagnosis, prognosis, and personalized treatment planning. This review highlights major categories of ML algorithms—supervised, unsupervised, semi-supervised, and reinforcement learning—and their applications in disease detection, medical imaging, genomics, and predictive analytics. It further examines the strengths and limitations of commonly used models such as decision trees, support vector machines, neural networks, and ensemble methods in the healthcare domain. The paper also discusses challenges including data privacy, model interpretability, and integration with existing healthcare systems, while exploring emerging trends such as federated learning and explainable AI. Overall, the study underscores the transformative potential of machine learning in enhancing patient outcomes, optimizing resource allocation, and supporting evidence-based medical practice.