چکیده مقاله
Heart disease prediction is a critical task regarding human health In order to drop its rate, effective and timely diagnosis of the disease is very essential Machine Learning methods have been developed to perform impressive predictions and make appropriate decisions So, simulated annealing search algorithm along with GMDH neural network is introduced to manage the features present in the earlier heart disease classification system The dimensionality of the features are reduced according to the behavior of simulated annealing search algorithm The selected features are processed by GMDH neural network classifier From the obtained results, the proposed model SA GMDH shows an increase in the classification accuracy by obtaining more than 89 58% when compared to the other feature selection methods
کلیدواژهها
نویسندگان
Nasibeh Emami
Department of Computer Science, Faculty of Engineering and Basic Sciences, Kosar University of Bojnord, Iran
شیوه ارجاع
Emami, Nasibeh,1400,Classification model for Statlog heart disease prediction through evolutionary feature selection and GMDH neural network,Fourth International Conference on Soft Computing
ارائهشده در
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