Record number :
2601137
Title of article :
Cuckoo inspired algorithms for feature selection in heart disease prediction
Author/Authors :
Usman , Ali Muhammad School of Computer Sciences - Universiti S ains Malaysia - Pulau Penang , Malaysia , Yusof , Umi Kalsom School of Computer Sciences - Universiti S ains Malaysia - Pulau Penang , Malaysia , Naim, Syibrah School of Computer Sciences - Universiti S ains Malaysia - Pulau Penang , Malaysia
Pages :
12
From page :
95
To page :
106
Abstract :
Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets.
Keywords :
Cuckoo optimization algorithm , Meta-heuristic algorithms , Feature selection , Cuckoo search , Heart disease
Journal title :
International Journal of Advances in Intelligent Informatics
Serial Year :
2018
Full Text URL :
Link To Document :
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