Comparative Analysis of Predicting the Diabetic Disease Using Machine Learning Techniques

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