Predicting Diabetes Using Machine Learning Techniques

Abstract

Early diagnosis of diabetes, which can cause death, is very important for the health of the person. In the literature, machine learning techniques are frequently used in diagnosis of many diseases, including diabetes. The aim of the study is to predict diabetes with high accuracy by using machine learning and preprocessing techniques. Pima Indian Diabetes dataset was used in the study. J48 (Decision Tree), Naïve Bayes, Support Vector Machine, Logistic Regression, Multilayer Perceptron, K Nearest Neighbor, Logistic Model Tree, and Random Forest were used for classification. Of the preprocessing methods, feature selection, imputing missing values, normalization and standardization are performed. According to the results obtained, the highest accuracy value got with the Random Forest algorithm as 80.869.