Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

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.

► We propose a novel method that hybridizes SVM and SA to the hepatitis diagnosis. ► The applied study dataset is hepatitis disease dataset from UCI repository. ► The obtained classification accuracy of our method is 96.25%. ► To illustrate different aspects of SVM-SA we applied it on two other benchmark datasets. ► The results show that SVM-SA method can assist in the diagnosis of hepatitis.