{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:46:02Z","timestamp":1780386362872,"version":"3.54.1"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:00:00Z","timestamp":1672185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Research and Innovation Agency of the Republic of Indonesia","award":["469.1\/UN27.22\/PT.01.03\/2022."],"award-info":[{"award-number":["469.1\/UN27.22\/PT.01.03\/2022."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Coronary heart disease is a type of cardiovascular disease characterized by atherosclerotic plaque, which causes myocardial infarction or sudden cardiac death. Since this sudden heart attack has no apparent symptoms, the early detection of the risk factors for coronary heart disease is required. Many studies have been conducted to diagnose heart disease, including studies that tested various classifiers, feature selection and detection models on several coronary heart disease datasets. As a result, this research aims to learn about the effect of the bee swarm optimization algorithm combined with Q-learning for optimizing the feature selection in improving the prediction of heart disease. This detection model was tested against various classification methods and evaluated against multiple performance measures, such as accuracy, precision, recall and the area under curve (AUC), to identify the best model for heart disease prediction and the benefit of the medical community. The test results show that the proposed method outperforms the existing process regarding the feature selection.<\/jats:p>","DOI":"10.3390\/info14010015","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:30:27Z","timestamp":1672205427000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Hybrid Model Feature Selection with the Bee Swarm Optimization Method and Q-Learning on the Diagnosis of Coronary Heart Disease"],"prefix":"10.3390","volume":"14","author":[{"given":"Yaumi A. Z. A.","family":"Fajri","sequence":"first","affiliation":[{"name":"Department of Informatics, Universitas Sebelas Maret, Surakarta 57126, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7014-7620","authenticated-orcid":false,"given":"Wiharto","family":"Wiharto","sequence":"additional","affiliation":[{"name":"Department of Informatics, Universitas Sebelas Maret, Surakarta 57126, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esti","family":"Suryani","sequence":"additional","affiliation":[{"name":"Department of Informatics, Universitas Sebelas Maret, Surakarta 57126, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1007\/s10916-016-0536-z","article-title":"A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data","volume":"40","author":"Verma","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kolukisa, B., Hacilar, H., Goy, G., Kus, M., Bakir-Gungor, B., Aral, A., and Gungor, V.C. (2018, January 10\u201313). Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622609"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9816142","DOI":"10.1155\/2020\/9816142","article-title":"Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble","volume":"2020","author":"Tama","year":"2020","journal-title":"BioMed Res. Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5267498","DOI":"10.1155\/2022\/5267498","article-title":"Cardiovascular Disease Detection using Ensemble Learning","volume":"2022","author":"Alqahtani","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Amin, S.U., Agarwal, K., and Beg, R. (2013, January 11\u201312). Genetic neural network based data mining in prediction of heart disease using risk factors. Proceedings of the 2013 IEEE Conference on Information and Communication Technologies, Thuckalay, India.","DOI":"10.1109\/CICT.2013.6558288"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.cmpb.2017.01.004","article-title":"Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm","volume":"141","author":"Arabasadi","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Subanya, B., and Rajalaxmi, R.R. (2014, January 13\u201314). Feature selection using Artificial Bee Colony for cardiovascular disease classification. Proceedings of the 2014 International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India.","DOI":"10.1109\/ECS.2014.6892729"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1504\/IJICA.2007.016796","article-title":"A selective approach to parallelise Bees Swarm Optimisation metaheuristic: Application to MAX-W-SAT","volume":"1","author":"Sadeg","year":"2007","journal-title":"IJICA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/978-3-319-19258-1_33","article-title":"BSO-FS: Bee Swarm Optimization for Feature Selection in Classification","volume":"Volume 9094","author":"Rojas","year":"2015","journal-title":"Advances in Computational Intelligence"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/978-3-030-20518-8_65","article-title":"QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection","volume":"Volume 11507","author":"Rojas","year":"2019","journal-title":"Advances in Computational Intelligence"},{"key":"ref_11","unstructured":"Dua, D., and Graff, C. (2022, April 05). UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/ml\/index.php."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.cmpb.2013.03.004","article-title":"A data mining approach for diagnosis of coronary artery disease","volume":"111","author":"Alizadehsani","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.4236\/jdaip.2020.82003","article-title":"Comparison of Different Machine Learning Algorithms for the Prediction of Coronary Artery Disease","volume":"08","author":"Dipto","year":"2020","journal-title":"JDAIP"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Akbari, R., Mohammadi, A., and Ziarati, K. (2009, January 14\u201315). A powerful bee swarm optimization algorithm. Proceedings of the 2009 IEEE 13th International Multitopic Conference, Islamabad, Pakistan.","DOI":"10.1109\/INMIC.2009.5383155"},{"key":"ref_15","first-page":"1","article-title":"Implementasi Q-Learning dan Backpropagation pada Agen yang Memainkan Permainan Flappy Bird","volume":"6","author":"Ardiansyah","year":"2017","journal-title":"J. Nas. Tek. Elektro Dan Teknol. Inf. (JNTETI)"},{"key":"ref_16","unstructured":"Nugroho, A.S., Witarto, A.B., and Handoko, D. (2022, April 09). Teori dan Aplikasinya dalam Bioinformatika1. Available online: http:\/\/asnugroho.net\/papers\/ikcsvm.pdf."},{"key":"ref_17","first-page":"272","article-title":"Random Forests and Decision Trees","volume":"9","author":"Ali","year":"2012","journal-title":"Int. J. Comput. Sci. Issues (IJCSI)"},{"key":"ref_18","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"65947","DOI":"10.1109\/ACCESS.2020.2985646","article-title":"An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo\u2013Tompa and Stacked Genetic Algorithm","volume":"8","author":"Ali","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"200131","article-title":"Cardiac disease detection using cuckoo search enabled deep belief network","volume":"16","author":"Nandakumar","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1016\/j.bbe.2020.09.005","article-title":"A Novel Approach for Coronary Artery Disease Diagnosis using Hybrid Particle Swarm Optimization based Emotional Neural Network","volume":"40","author":"Shahid","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gupta, A., Arora, H.S., Kumar, R., and Raman, B. (2021, January 13\u201316). DMHZ: A Decision Support System Based on Machine Computational Design for Heart Disease Diagnosis Using Z-Alizadeh Sani Dataset. Proceedings of the 2021 International Conference on Information Networking (ICOIN), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICOIN50884.2021.9333884"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106628","DOI":"10.1016\/j.compeleceng.2020.106628","article-title":"Support Vector Machines-based Heart Disease Diagnosis using Feature Subset, Wrapping Selection and Extraction Methods","volume":"84","author":"Shah","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sarra, R.R., Dinar, A.M., Mohammed, M.A., and Abdulkareem, K.H. (2022). Enhanced Heart Disease Prediction Based on Machine Learning and \u03c72 Statistical Optimal Feature Selection Model. Designs, 6.","DOI":"10.3390\/designs6050087"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/1\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:53:26Z","timestamp":1760147606000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/1\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,28]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["info14010015"],"URL":"https:\/\/doi.org\/10.3390\/info14010015","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,28]]}}}