Comparative analysis of predicting the diabetic disease using machine learning techniques

Abstract

Machine Learning is concerned with the making of calculations and methods that use PCs to learn and acquire insight, using the related knowledge available.This work is focused on machine learning approaches for predicting diabetic disorders, using datasets from Predict the Diabetic Diseases. A web-based comparative analysis of multiple machine learning algorithms (Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression) is utilized in this paper, to assess their performances in recognizing reliable models for detecting diabetic disease. To see the effects of adding more features to the classification model, three performance measures were chosen: F1-Measure, Precision, and Accuracy.