Performance Analysis Of Machine Learning Techniques To Predict Diabetes Mellitus

Abstract

Nowadays Diabetes is the most common disease caused due to metabolic disorders the sugar levels are in excess of long-drawn-out stage. It affects the different organs of the body particularly in blood veins and nerves. Through the prediction, the disease can be controlled and can save the human life. This work analysis the performance of different machine learning techniques like Decision Tree, SVM, KNN, Logistic Regression, Naïve Bayes, Random Forest for predicting models from the diagnostic medical datasets. The extracted knowledge is used to predict the diabetic patients easily. The experimental results shows that the Random Forest algorithm work well and providing good precision, recall and accuracy compared to other machine learning algorithms.