An accurate diagnosis of diseases like hepatitis is a challenging task for physicians. This problem in diagnosis has attracted researchers to design medical expert systems with utmost accuracy. This paper proposes a clinical decision support system based on Support Vector Machine (SVM) and hybrid Genetic Algorithm (GA) –Simulated Annealing (SA) for the diagnosis of hepatitis by using the dataset of UCI machine learning repository. The SVM with Gaussian Radial Basis Function (RBF) kernel performs the classification process. The hybrid GA-SA is used for two purposes, one is to select the most significant feature subset of the dataset, and the other is to optimize the kernel parameters of SVM. The performance of the expert system is analyzed using various parameters like classification accuracy, sensitivity and specificity. The classification accuracy of the proposed system is found to be superior to that of the other existing systems in the literature.

S. Anto,S. Chandramathi."An Expert System based on SVM and Hybrid GA-SA Optimization for Hepatitis Diagnosis". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 07,pp.437-443, July - 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I704.pdf,

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