Classification of diabetes using deep learning and svm techniques

Abstract

Background: Diabetes is a disease increasingly alarming affecting people worldwide. If not treated properly, it affects organs. An automatic classification of diabetes using deep learning perceptron and SVM is attempted. Objective: To identified problem and provide automatic classification of diabetics data and the solution using deep learning perceptron and SVM for the best therapeutic management. Method: The dataset consists of 768 instances, out of which, 500 diabetes subjects and 268 healthy people. Results: There are 9 attributes which are used for analysis. MLP deep learning classifier with 18 parameters are utilized to correctly classify 595 Instances with a classification accuracy of 77.474 % and Incorrectly Classified are 173 Instances with 22.526 %. The comparison of MLP deep learning classifier with SVM classifier is performed where the SVM classifier Correctly Classified Instances are 500 with a classification accuracy of 65.1042 % and Incorrectly Classified Instances are 268 with 34.8958 %. Conclusion: Deep learning perceptron classifier performs well with diabetes dataset and can be used for further automatic identification and detection analysis.