S. Anto, S. Chandramathi, B. Femina



An Expert System based on SVM and Hybrid GA-SA Optimization for Disease Diagnosis

pdf PDF


An accurate diagnosis of diseases by physicians is a tedious and challenging task. This challenge can be addressed by designing and implementing medical expert systems with utmost accuracy. This paper proposes a medical expert system based on Support Vector Machine (SVM) and hybrid Genetic Algorithm (GA) –Simulated Annealing (SA) for the diagnosis of a set of diseases 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 the selection of the most significant feature subset of the dataset and for the optimization of the kernel parameters of SVM. The performance of the expert system is analyzed using various parameters like classification accuracy, sensitivity and specificity. The proposed system is validated using different disease dataset like Pima Indian Diabetes (PID), breast cancer, hepatitis and cardiac arrhythmia. The classification accuracy of the proposed system is found to be superior to that of the other existing systems in the literature.


Medical Expert System, Machine Learning, Genetic Algorithm, Simulated Annealing, Support Vector Machine.


[1] M. Pradhan, R. K. Sahu, Predict the onset of diabetes disease using Artificial Neural Network (ANN), Int. J. Comput. Sci. Emerg .Technol .2, 2011, pp.303–311.

[2] Boyle, James P., Amanda A. Honeycutt, KM Venkat Narayan, Thomas J. Hoerger, Linda S. Geiss, Hong Chen, and Theodore J. Thompson, Projection of Diabetes Burden Through 2050 Impact of changing demography and disease prevalence in the US, Diabetes care 24, No. 11, 2001, pp.1936-1940.

[3] R. Siegel, D. Naishadham, & A. Jemal, Cancer statistics, CA: A Cancer Journal for Clinicians, Vol.62, 2012, pp.10–29.

[4] J.Cohen, The scientific challenge of hepatitis C, Science, Vol.285, 1999, pp.26–30.

[5] Varma, Kamadi VSRP, Allam Appa Rao, T. Sita Maha Lakshmi, and PV Nageswara Rao, A computational intelligence approach for a better diagnosis of diabetic patients, Computers & Electrical Engineering, Vol.40, No. 5, 2014, pp.1758-1765.

[6] Seera, Manjeevan, and Chee Peng Lim, A hybrid intelligent system for medical data classification, Expert Systems with Applications 41, No. 5, 2014, pp.2239-2249.

[7] Cslisir, Duygu, and Esin Dogantekin, A new intelligent hepatitis diagnosis system: PCA–LSSVM, Expert Systems with Applications 38, No. 8, 2011, pp.10705-10708.

[8] 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, 2013, pp.3429-3438.

[9] 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, 2013, 2677-2686.

[10] Zheng, Bichen, Sang Won Yoon, and Sarah S. Lam, Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms, Expert Systems with Applications 41, No. 4, 2014, pp1476-1482.

[11] Ozcift, Akin, Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis, Computers in Biology and Medicine 41, No. 5, 2011, pp.265-271.

[12] L.H. Cheng, J.W. Chieh, A GA - Based Feature Selection and Parameters Optimization for Support Vector Machines, Expert Systems with Applications, Elsevier, Vol.31, 2006, pp.231–240.

[13] D.E. Goldberg, Genetic Algorithm in Search, Optimization, and Machine Learning, Addison-Wesley, Boston, 1989.

[14] Javad Salimi Sartakhti, Mohammad Hossein Zangooei, 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, 2012, pp.570-579.

[15] S. Idicula Thomas, A.J. Kulkarni, B.D. Kulkarni, V.K. Jayaraman, & 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, 2006, pp.278–284.

[16] Vapnik V, Support-vector networks, Machine Learning, 20, 1995, pp.273–297.

[17] 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, 1998, pp. 185–208.

[18] S.S. Keerthi, & C.J. Lin, Asymptotic behaviors of support vector machines with Gaussian kernel, Neural Computation, 15, 2003, pp.1667–1689.

[19] Zangooei, Mohammad Hossein, Jafar Habibi, and Roohallah Alizadehsani, Disease Diagnosis with a hybrid method SVR using NSGA-II, Neurocomputing, 136,2014, pp.14-29.

[20] Pradhan, Manaswini, and Ranjit Kumar Sahu, Predict the onset of diabetes disease using Artificial Neural Network (ANN), International Journal of Computer Science & Emerging Technologies (E-ISSN: 2044-6004), 2, No. 2 , 2011.

[21] Barakat, Nahla, P. Andrew Bradley, and H. Mohamed Nabil, Barakat, Intelligible support vector machines for diagnosis of diabetes mellitus, Information Technology in Biomedicine, IEEE Transactions on 14, No. 4, 2010, pp.1114-1120.

[22] Huang, Cheng-Lung, and Chieh-Jen Wang, A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with applications 31, No. 2 , 2006, pp.231-240.

[23] 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, 2010, pp.1248-1255.

[24] Jadhav, M. Shivajirao, L. Sanjay, Nalbalwar, and A. Ashok, Ghatol, Artificial neural network based cardiac arrhythmia disease diagnosis, In Process Automation, Control and Computing (PACC), 2011 International Conference on, IEEE, 2011, pp. 1-6.

[25] Mitra, Malay, and R. K. Samanta, Cardiac arrhythmia classification using neural networks with selected features, Procedia Technology 10, 2013, pp.76-84.

[26] M Jadhav, Shivajirao, Sanjay L Nalbalwar, and Ashok A Ghatol, Artificial neural network models based cardiac arrhythmia disease diagnosis from ECG signal data, International Journal of Computer Applications 44, No. 15, 2012, pp. 8-13.

[27] K.C. Tan, E.J. Teoh, Q. Yua, K.C. Goh, A hybrid evolutionary algorithm for attribute selection in data mining, Expert Systems with Applications, Elsevier, 2009.

[28] S.S. Javad, H.Z. Mohammad, M. Kourosh, Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA), Computer Methods and Programs in Biomedicine.8, 2012, pp.570-579.

[29] A.F. Atiya, A. Al-Ani, A penalized likelihood based pattern classification algorithm, Pattern Recogn, 42, 2009, pp.2684–2694.

[30] 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/ e-library/ conferences/ digest2003/ papers, pp. 463-104.

[31] L. Ozyilmaz, T. Yildirim, Artificial neural networks for diagnosis of hepatitis disease, International joint conference on Neural Networks (IJCNN), Portland, OR, USA, July20–24, IEEE, 2003, vol.581, pp.586–589.

[32] B. Ster, Dobnikar, A Neural network in medical diagnosis: comparison with other methods. EANN’96, 1996, pp. 427–430.

Cite this paper

S. Anto, S. Chandramathi, B. Femina. (2017) An Expert System based on SVM and Hybrid GA-SA Optimization for Disease Diagnosis. International Journal of Biology and Biomedicine, 2, 86-97


Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0