Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA) |
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Authors: | Sartakhti Javad Salimi Zangooei Mohammad Hossein Mozafari Kourosh |
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Affiliation: | SCS Lab, Electrical and Computer Engineering Department, Tarbiat Modares University, Terhran, Iran. Electronic address: salimi.sartakhti@gmail.com. |
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Abstract: | In this study, diagnosis of hepatitis disease, which is a very common and important disease, is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM) and simulated annealing (SA). Simulated annealing is a stochastic method currently in wide use for difficult optimization problems. Intensively explored support vector machine due to its several unique advantages is successfully verified as a predicting method in recent years. We take the dataset used in our study from the UCI machine learning database. The classification accuracy is obtained via 10-fold cross validation. The obtained classification accuracy of our method is 96.25% and it is very promising with regard to the other classification methods in the literature for this problem. |
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