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

Tópicos

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Resumo

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.

Resumo Limpo

studi diagnosi hepat diseas common import diseas conduct machin learn method propos novel machin learn method hybrid support vector machin svm simul anneal sa simul anneal stochast method current wide use difficult optim problem intens explor support vector machin due sever uniqu advantag success verifi predict method recent year take dataset use studi uci machin learn databas classif accuraci obtain via fold cross valid obtain classif accuraci method promis regard classif method literatur problem

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