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http://dx.doi.org/10.6109/jicce.2022.20.1.31

Hybrid Feature Selection Method Based on Genetic Algorithm for the Diagnosis of Coronary Heart Disease  

Wiharto, Wiharto (Department of Informatics, Sebelas Maret University)
Suryani, Esti (Department of Informatics, Sebelas Maret University)
Setyawan, Sigit (Department of Medicine, Sebelas Maret University)
Putra, Bintang PE (Department of Informatics, Sebelas Maret University)
Publication Information
Journal of information and communication convergence engineering / v.20, no.1, 2022 , pp. 31-40 More about this Journal
Abstract
Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.
Keywords
coronary heart disease; genetic algorithm; feature selection; ensemble learning; support vector machine;
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