MACHINE LEARNING ALGORITHMS IN EARLY DISEASE DETECTION: A COMPARATIVE ANALYSIS

Early disease detection is one of the most important objectives of modern healthcare, as timely diagnosis can improve treatment outcomes, reduce complications, and support more effective clinical decision-making. In recent years, machine learning has emerged as a promising approach for identifying disease patterns in medical data and assisting healthcare professionals in recognizing potential health risks at an earlier stage. This article provides a comparative analysis of major machine learning algorithms used in early disease detection, including logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, and artificial neural networks. Their principal characteristics, advantages, limitations, and potential clinical applications are examined in relation to diagnostic accuracy, interpretability, computational complexity, and data requirements. The article also discusses challenges associated with medical data quality, class imbalance, algorithmic bias, privacy, and clinical validation. The analysis indicates that no single algorithm is optimal for every diagnostic task. Instead, the choice of an appropriate model depends on the disease under investigation, the characteristics of the available dataset, the consequences of false-positive and false-negative predictions, and the practical requirements of healthcare settings. Responsible integration of machine learning into clinical workflows may strengthen early detection systems, provided that algorithmic predictions are validated and interpreted within an appropriate medical context.