title: Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA).
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.
authors.affiliation: SCS Lab, Electrical and Computer Engineering Department, Tarbiat Modares University, Terhran, Iran. salimi.sartakhti@gmail.com
authors.forename: Javad Salimi
authors.initials: JS
authors.lastname: Sartakhti
authors.forename: Mohammad Hossein
authors.initials: MH
authors.lastname: Zangooei
authors.forename: Kourosh
authors.initials: K
authors.lastname: Mozafari
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D003198:Computer Simulation; D005260:Female; D006525:Hepatitis, Viral, Human / Q000175:diagnosis*; D006801:Humans; D008297:Male; D008875:Middle Aged; D060388:Support Vector Machine