Machine Learning Tree Classifiers in Predicting Diabetes Mellitus

Diabetes Mellitus (DM) is the group of diseases where the patient suffers from higher levels of sugar in blood over a prolonged time. Machine learning classifier helps to predict the disease based on the condition of the symptom suffered by the patient. The aim of this paper is to compare the performance of the machine learning tree classifiers in predicting Diabetes Mellitus (DM). Machine learning tree classifiers such as Random Forest, C4.5, Random Tree, REPTree, and Logistic Model Tree (LMT) were analyzed based on their accuracy and True Positive Rate (TPR). In this analysis of predicting diabetes mellitus Logistic Model Tree (LMT) machine learning tree classifier achieved higher accuracy of 79.31%, True Positive Rate (TPR) of 0.739 and an execution time of 1.09 sec than other classifiers under study.