Enhancing the Quality and Efficiency of Mental Health Care using Decision Trees

Healthcare sector requires the need to advance with the newer technologies blooming and prospering. In this sector where decision making in circumstances of diagnosing, classifying and successful predicting is challenging, the Hoeffding Decision tree (HDT) or simply decision trees (DT) play a vital role. These provide wider scope and clarity alongside deeper precision. Decision making trees are one of the chief Data mining techniques that can enhance the reliability in heath industry. In this paper, the features of decision trees and its suitable future application in the field of medicine are discussed. It includes a detailed study of the methods and algorithms such as the C4.5 algorithm and the Clinical decision analysis (CDA) method as a convenient way of expressing the information to implement the decision trees. Further, decision trees present a valuable approach for clinical decision making even in mental health because of their interpretability, capacity to spot at-risk patients, and support in treatment planning. Therefore, decision trees are effective tools for improving the effectiveness and quality of mental health care.