Analysis of diabetes based on Tree Decision, Logistic Regression and Artificial Neural Network

Abstract

The alarming rise in diabetes cases globally, particularly amongst the aging population, underscores a pressing need for innovative, accurate, and early diagnostic tools. Amidst this health crisis, the present study delves into the evaluation of three prominent machine learning in predicting diabetes using the Pima Indians Diabetes Database. A comprehensive analysis, considering metrics such as accuracy, precision, recall, F1 score, and ROC curve, revealed Decision Trees as the most efficacious, exhibiting adeptness in balancing precision and recall. Artificial Neural Networks displayed a remarkable ability to identify true positive cases, making it a notable contender in medical diagnostics. In terms of accuracy, Decision Trees led with a rate of 76.19%, followed by Logistic Regression at 74.03%, and Artificial Neural Networks trailing at 72.64%. The findings suggest Decision Trees' potential utility in offering both a nuanced and comprehensive approach to diabetes prediction. However, the constraints imposed by the dataset, and the inherent limitations and sensitivities of the machine learning models, call for a judicious interpretation of the results. This research illuminates the prospective integration of machine learning in healthcare for enhanced, personalized, and timely diabetes management, paving the way for improved patient outcomes and a substantial reduction in global health disparities attributed to diabetes.