Predictive Modeling and Analysis of Diabetes using Machine Learning

Disease management and prediction have greatly improved with the growing use of Machine Learning (ML) techniques in healthcare. In order to avoid serious consequences, Diabetes Mellitus, a common chronic disorder, requires a precise and timely diagnosis. In addition to providing an analysis of current ML based techniques for prediction of diabetes, this work includes an empirical case study utilizing the PIMA Indian Diabetes Dataset, which consists of 768 patient data classified into eight characteristics (pregnancy, glucose, blood pressure, skin thickness, insulin, BMI, and function of the diabetes pedigree). Four classifiers were used: nearest neighbors (KNN), random forest, decision tree, and support vector machine (SVM) and their diagnostic precision was assessed. With the best accuracy of 76.62%, the SVM was followed by KNN (66. 23%), Random Forest (72. 07%) and Decision Tree (75.97%). Finding successful models for early diabetes prediction by combining empirical validation with a literature-based comparison analysis is what makes this work novel. Additional clinical and lifestyle parameters should be included in future research to improve model generality and practicality.