Introduction: Diabetes is a disease associated with high levels of glucose in the blood. Diabetes make many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The aim of this study is to diagnose Diabetes with machine learning techniques.Material and Methods: The datasets of the article contain several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age. The main objective of the machine learning models is to classify of the diabetes disease.Results: six classifiers have been also adapted and compared their performance based on accuracy, F1-score, recall, precision and AUC. And Finally, Adaboost has the most accuracy 83%.Conclusion: In this paper a performance comparison of different classifier models for classifying diagnosis is done. The models considered for comparison are logistic regression, Decision Tree, support vector machine (SVM), xgboost, Random forest and ada boost. Finally, in the comparison flow, Adaboost, Logistic Regression, SVM and Random Forest, usually has had a high amount; and their amounts has little differences normally.

Introduction: Diabetes is a chronic disease associated with abnormal high levels of glucose in the blood. Diabetes make many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The goal of this study is to Predict hospital readmission of Diabetic patients with machine learning techniques.Material and Methods: The data used in the study are data obtained from the UCI Machine Learning Repository about diabetic patients. The dataset used contains 100,000 instances and it include 55 features from 130 hospitals in the United States for 10 years.Results: This article gets results from the final stages of evaluation. In this evaluation process, compared the performance of Decision tree, Random forest, Xgboost, k-Neighbors, adaboost and deep neural network with accuracy.Conclusion: The number of selected features by PCA-based feature selection method improve the predictive performance based on accuracy of deep learning and most machine learning models for predicting readmission. The improvement of machine learning models depended on the specific choice of the prediction model, number of selected features, and “k” for k-fold validation.