A Review on Decision Tree Algorithm in Healthcare Applications

Abstract

Decision tree algorithms have emerged as a pivotal tool in healthcare, offering substantial benefits in diagnostics, prognosis, and health monitoring. This paper provides a comprehensive review of decision tree applications in medical settings, highlighting their ability to simplify complex decision-making processes and improve accuracy in disease diagnosis and outcome prediction. By dissecting various research studies and clinical implementations, we demonstrate the versatility of decision trees in handling diverse datasets—from genetic markers to electronic health records and real-time patient data. This review also explores the integration of decision trees with machine learning techniques to enhance diagnostic procedures and prognostic evaluations, underscoring the significant role of these algorithms in advancing personalized medicine and public health strategies. Challenges such as data sensitivity, privacy concerns, and the need for large annotated datasets are discussed to provide a balanced perspective on the capabilities and limitations of decision tree algorithms in healthcare. Through this analysis, we aim to illuminate the transformative potential of decision trees in improving patient care and streamlining healthcare operations.