Prediction and Diagnosis of Diabetes by Using Data Mining Techniques

Background: Diabetes mellitus (DM) is one of the most common diseases in the world. Complications of this disease include nephropathy, cardiac arrest, blindness, and even mutilation of the body. The accurate diagnosis of this condition is very important. Objectives: This study was to identify and provide a model for diagnosis of DM using data mining. Methods: The data used in this study were obtained from ۷۶۸ women aged ۲۱-۸۳ year old. Nine variables were selected for investigation. The neural network, Basin network, C۵.۰, and support vector machine models were compared for predicting diabetes and their precision to this end. Clementine ۱۲ software was used to analyze the data. Results: The proposed method for classification of records with the C۵.۰ algorithm for accuracy data is ۸۰.۲% and for accuracy data ۸۷.۵%. In comparison with similar studies, it was better to diagnose people with diabetes, while glucose, body mass index and age variables were important in this study. Conclusion: The C۵.۰ algorithm showed the highest value of accuracy, specificity, and sensitivity compared with other methods studied. Therefore, the C۵.۰ algorithm probably performs the best classification among other algorithms and is recommended as the best method for diabetes prediction using available data.