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,
: Medical Expert System, Machine Learning, Genetic Algorithm, Simulated Annealing, Support Vector Machine.
 Cohen, Jon, "The scientific challenge of hepatitis C," Science 285, no. 5424, pp. 26-30, 1999.
 Seera, Manjeevan, and Chee Peng Lim, "A hybrid intelligent system for medical data classification," Expert Systems with Applications 41, no. 5, pp. 2239-2249, 2014.
 Çalişir, Duygu, and Esin Dogantekin, "A new intelligent hepatitis diagnosis system: PCA–LSSVM," Expert Systems with Applications 38, no. 8, pp. 10705-10708, 2014.
 Kaya, Yılmaz, and Murat Uyar, "A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease," Applied Soft Computing 13, no. 8, pp. 3429-3438, 2013.
 Stoean, Ruxandra, and Catalin Stoean, "Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection," Expert Systems with Applications 40, no. 7, pp. 2677-2686, 2013.
 Huang, Cheng-Lung, and Chieh-Jen Wang, "A GA-based feature selection and parameters optimizationfor support vector machines," Expert Systems with applications 31, no. 2, pp. 231-240, 2006.
 Golberg, David E, "Genetic algorithms in search, optimization, and machine learning." Addion wesley 1989.
 Sartakhti, Javad Salimi, Mohammad Hossein Zangooei, and Kourosh Mozafari, "Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)," Computer methods and programs in biomedicine 108, no. 2, pp. 570-579, 2012.
 Idicula-Thomas, Susan, J. Abhijit Kulkarni, D. Bhaskar Kulkarni, V. K. Jayaraman, and P. V. Balaji, "A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli." Bioinformatics 22, no. 3, pp. 278-284, 2006.
 Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks." Machine learning 20, no. 3, pp. 273-297, 1995.
 J. Platt, “Sequential minimal optimization: A fast algorithm for training support vector machines. In Advances In Kernel Methods – Support Vector Learning,” Cambridge, MA, USA: MIT Press, pp. 185–208, 1998.
 Keerthi, S. Sathiya, and Chih-Jen Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel," Neural computation 15, no. 7, pp. 1667-1689, 2003.
 Barakat, H. Mohamed Nabil, and P. Andrew Bradley, "Intelligible support vector machines for diagnosis of diabetes mellitus," Information Technology in Biomedicine, IEEE Transactions on 14, no. 4, pp. 1114-1120, 2010.
 Dogantekin, Esin, Akif Dogantekin, Derya Avci, and Levent Avci, "An intelligent diagnosis system for diabetes on linear discriminant analysis and adaptive network based fuzzy inference system: LDA-ANFIS," Digital Signal Processing 20, no. 4, pp. 1248-1255, 2010.
 Jadhav, M. Shivajirao , L. Sanjay Nalbalwar, and Ashok Ghatol, "Artificial neural network based cardiac arrhythmia disease diagnosis," In Process Automation, Control and Computing (PACC), 2011 International Conference on, pp. 1-6. IEEE, 2011.
 Tan, Kay Chen, Eu Jin Teoh, Q. Yu, and K. C. Goh. "A hybrid evolutionary algorithm for attribute selection in data mining," Expert Systems with Applications 36, no. 4 , pp. 8616-8630, 2009.
 Sartakhti, Javad Salimi, Mohammad Hossein Zangooei, and Kourosh Mozafari, "Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)," Computer methods and programs in biomedicine 108, no. 2 , pp. 570-579, 2012.
 Atiya, Amir F., and Ahmed Al-Ani, "A penalized likelihood based pattern classification algorithm," Pattern Recognition 42, no. 11, pp. 2684-2694, 2009.
 Hassanien, Aboul Ella, and Jafar MH Ali. "Feature extraction and rule classification algorithm of digital mammography based on rough set theory," In Available at www. wseas. us/ elibrary/ conferences/ digest2003/ papers, pp. 463-104.
 Ozyilmaz, Lale, and Tulay Yildirim. "Artificial neural networks for diagnosis of hepatitis disease," In Neural Networks, 2003. Proceedings of the International Joint Conference on, vol. 1, pp. 586-589. IEEE, 2003.