Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms

Diabetes mellitus (DM) is a severe worldwide health problem, and its prevalence is quickly growing. It is a spectrum of metabolic illnesses definite by continually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of difficulties, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be mainly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to detect the strong ML algorithm to forecast it. For this numerous ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to this work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the research was implemented using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This research discussed performance matrices and error rates of classifiers for both datasets. The outcomes showed that for the PID database (PIDD), SVM works improved with an accuracy of 74% whereas for Germany RF and KNN work improved with 98.7% accuracy. This study can helps healthcare facilities and researchers in understanding the value and application of ML algorithms in predicting diabetes at an initial stage