{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T16:49:23Z","timestamp":1791478163464,"version":"4.3.4"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Food and Drug Safety","award":["22213MFDS3922"],"award-info":[{"award-number":["22213MFDS3922"]}]},{"name":"Ministry of Food and Drug Safety","award":["2022R1A2C2006326"],"award-info":[{"award-number":["2022R1A2C2006326"]}]},{"name":"Ministry of Food and Drug Safety","award":["IITP-2022-2020-0-01612"],"award-info":[{"award-number":["IITP-2022-2020-0-01612"]}]},{"name":"National Research Foundation of Korea","award":["22213MFDS3922"],"award-info":[{"award-number":["22213MFDS3922"]}]},{"name":"National Research Foundation of Korea","award":["2022R1A2C2006326"],"award-info":[{"award-number":["2022R1A2C2006326"]}]},{"name":"National Research Foundation of Korea","award":["IITP-2022-2020-0-01612"],"award-info":[{"award-number":["IITP-2022-2020-0-01612"]}]},{"name":"Ministry of Science and ICT","award":["22213MFDS3922"],"award-info":[{"award-number":["22213MFDS3922"]}]},{"name":"Ministry of Science and ICT","award":["2022R1A2C2006326"],"award-info":[{"award-number":["2022R1A2C2006326"]}]},{"name":"Ministry of Science and ICT","award":["IITP-2022-2020-0-01612"],"award-info":[{"award-number":["IITP-2022-2020-0-01612"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician\u2019s skill and judgment. Several studies have shown promising results in automatically detecting cardiovascular disorders based on heart-sound signals. However, the accuracy performance needs to be enhanced as automated heart-sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart-sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the PhysioNet Challenge 2016 and 2022 datasets, feature extraction using Mel frequency cepstrum coefficients (MFCC), and classification using grid search for hyperparameter tuning of several classifier algorithms including k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The five-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained classification accuracy of 95.78% and 76.31%, which was assessed using PhysioNet Challenge 2016 and 2022, respectively. The findings demonstrate that the suggested approach obtained excellent classification results using PhysioNet Challenge 2016 and showed promising results using PhysioNet Challenge 2022. Therefore, the proposed method has been potentially developed as an additional tool to facilitate the medical practitioner in diagnosing the abnormality of the heart sound.<\/jats:p>","DOI":"10.3390\/bioengineering10010045","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:19:46Z","timestamp":1672370386000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning"],"prefix":"10.3390","volume":"10","author":[{"given":"Yunendah Nur","family":"Fuadah","sequence":"first","affiliation":[{"name":"Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea"},{"name":"School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Adnan","family":"Pramudito","sequence":"additional","affiliation":[{"name":"Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ki Moo","family":"Lim","sequence":"additional","affiliation":[{"name":"Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea"},{"name":"Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea"},{"name":"Meta Heart Co., Ltd., Gumi 39177, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, W., Sun, Q., Chen, X., Xie, G., Wu, H., and Xu, C. 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