Title: An expert system based on LS-SVM and simulated annealing for the diagnosis of diabetes disease

Authors: S. Anto; S. Chandramathi; S. Aishwarya

Addresses: Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India ' Department of Electronics and Communication Engineering, Sri Krishna College of Technology, Coimbatore, India ' Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, India

Abstract: Clinical decision making is a complex task for physicians as it requires utmost accuracy of diagnosis. This paper proposes a medical decision support system based on least square support vector machine (LS-SVM) and simulated annealing (SA) heuristic for the diagnosis of diabetes in the Pima Indian diabetes (PID) dataset of the UCI machine learning repository. Fisher score (FS) algorithm is used to select the most significant features from the given feature set. LS-SVM with radial basis function (RBF) is used for classification and the SA for optimisation of the kernel parameters of the LS-SVM. The performance of the proposed system is analysed in terms of classification accuracy, sensitivity and specificity using ten-fold cross-validation and confusion matrix. The results show that the classification accuracy of the proposed system outperforms that of various existing systems.

Keywords: least squares SVM; support vector machines; LS-SVM; simulated annealing; classification accuracy; sensitivity; specificity; expert systems; diabetes diagnosis; medical DSS; decision support systems; radial basis function; RBF; optimisation.

DOI: 10.1504/IJICT.2016.077693

International Journal of Information and Communication Technology, 2016 Vol.9 No.1, pp.88 - 100

Received: 31 Oct 2013
Accepted: 10 Oct 2014

Published online: 13 Jul 2016 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article