{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T10:23:34Z","timestamp":1782210214418,"version":"3.54.5"},"reference-count":62,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004595","name":"Universiti Sains Malaysia","doi-asserted-by":"publisher","award":["304\/PKOMP\/6315435"],"award-info":[{"award-number":["304\/PKOMP\/6315435"]}],"id":[{"id":"10.13039\/501100004595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.<\/jats:p>","DOI":"10.1177\/20552076221150741","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T18:09:14Z","timestamp":1673460554000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":17,"title":["Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony"],"prefix":"10.1177","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4705-2732","authenticated-orcid":false,"given":"Pantea","family":"Keikhosrokiani","sequence":"first","affiliation":[{"name":"School of Computer Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. Bhanupriya","family":"Naidu A\/P Anathan","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzi","family":"Iryanti Fadilah","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Selvakumar","family":"Manickam","sequence":"additional","affiliation":[{"name":"National Advanced IPv6 Centre, Universiti Sains Malaysia, Minden, Penang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuoyong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Minjiang University, Fuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"bibr1-20552076221150741","unstructured":"The top 10 causes of death. World Health Organization. 2020."},{"key":"bibr2-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2017.11.006"},{"key":"bibr3-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s10111-019-00565-4"},{"key":"bibr4-20552076221150741","doi-asserted-by":"publisher","DOI":"10.4018\/IJEA.2021070104"},{"key":"bibr5-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2021.108152"},{"key":"bibr6-20552076221150741","doi-asserted-by":"crossref","volume-title":"Swarm intelligence and bio-inspired computation: theory and applications","author":"Yang X-S","DOI":"10.1016\/B978-0-12-405163-8.00001-6"},{"key":"bibr7-20552076221150741","doi-asserted-by":"publisher","DOI":"10.3390\/e23060667"},{"key":"bibr8-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1002\/chp.1340220304"},{"key":"bibr9-20552076221150741","unstructured":"Keikhosrokiani P. Big data analytics for healthcare: datasets, techniques, life cycles, management, and applications. San Diego, CA: Elsevier Science, 2022, p. 354."},{"key":"bibr10-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0064995"},{"key":"bibr11-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2021.100194"},{"key":"bibr12-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1916\/1\/012015"},{"key":"bibr13-20552076221150741","first-page":"7","volume":"14","author":"Ziasabounchi N","year":"2014","journal-title":"International Journal of Electrical & Computer Sciences IJECS-IJENS"},{"key":"bibr14-20552076221150741","first-page":"7421","volume":"2582","author":"Arya S","year":"2021","journal-title":"Journal Homepage: www ijrpr com ISSN"},{"key":"bibr15-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-33-6773-9"},{"key":"bibr16-20552076221150741","doi-asserted-by":"crossref","unstructured":"Vaishali G, Kalaivani V. Big data analysis for heart disease detection system using map reduce technique. In: 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16), 2016, pp.1\u20136.","DOI":"10.1109\/ICCTIDE.2016.7725360"},{"key":"bibr17-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehx284"},{"key":"bibr18-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s11897-020-00469-9"},{"key":"bibr19-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-30779-9_44"},{"key":"bibr20-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3066365"},{"key":"bibr21-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ab8770"},{"key":"bibr22-20552076221150741","doi-asserted-by":"publisher","DOI":"10.3390\/s19214819"},{"key":"bibr23-20552076221150741","unstructured":"Gomes EF, Pereira E. Classifying heart sounds using peak location for segmentation and feature construction.\n                      Workshop Classifying Heart Sounds\n                      2012: 480\u201392."},{"key":"bibr24-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1186\/1475-925X-10-13"},{"key":"bibr25-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0112673"},{"key":"bibr26-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1109\/21.256541"},{"key":"bibr27-20552076221150741","unstructured":"Bobillo IJD. A tensor approach to heart sound classification. In: 2016 Computing in Cardiology Conference (CinC), 2016, pp.629\u2013632: IEEE."},{"key":"bibr28-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-811534-3.00003-2"},{"key":"bibr29-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01928-5"},{"key":"bibr30-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1142\/9548"},{"key":"bibr31-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3005614"},{"key":"bibr32-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-019-04386-5"},{"key":"bibr33-20552076221150741","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-190207"},{"key":"bibr34-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-021-02331-z"},{"key":"bibr35-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2021.126944"},{"key":"bibr36-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1080\/03772063.2020.1844079"},{"key":"bibr37-20552076221150741","doi-asserted-by":"publisher","DOI":"10.3390\/su13084576"},{"key":"bibr38-20552076221150741","unstructured":"Ma L, Li N, Guo Y, et al. Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System.\n                      IEEE Transactions on Cybernetics\n                      . 2021: 1\u201314."},{"key":"bibr39-20552076221150741","volume-title":"Metaheuristic optimization: Nature-inspired algorithms swarm and computational intelligence, theory and applications","author":"Okwu MO","year":"2020"},{"key":"bibr40-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-27705-6_6"},{"key":"bibr41-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-33869-7_1"},{"key":"bibr42-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-014-1498-z"},{"key":"bibr43-20552076221150741","first-page":"1","volume":"9","author":"Tereshko V","year":"2005","journal-title":"Comput Inf Syst"},{"key":"bibr44-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2886629"},{"key":"bibr45-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-017-9610-2"},{"key":"bibr46-20552076221150741","doi-asserted-by":"crossref","unstructured":"Karaboga D, Kaya E. Training ANFIS using artificial bee colony algorithm for nonlinear dynamic systems identification. In: 2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014, pp.493\u2013496.","DOI":"10.1109\/SIU.2014.6830273"},{"key":"bibr47-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-19562-5_4"},{"key":"bibr48-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2723483"},{"key":"bibr49-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-817657-3.00005-5"},{"key":"bibr50-20552076221150741","volume-title":"Perspectives in the development of mobile medical information systems: Life cycle, management, methodological approach and application","author":"Keikhosrokiani P","year":"2019"},{"key":"bibr51-20552076221150741","doi-asserted-by":"publisher","DOI":"10.4018\/IJITSA.307019"},{"key":"bibr52-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-97929-4_16"},{"key":"bibr53-20552076221150741","unstructured":"Keikhosrokiani P, Mustaffa N, Zakaria N, et al. User behavioral intention toward using mobile healthcare"},{"key":"bibr54-20552076221150741","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-5225-6198-9"},{"key":"bibr55-20552076221150741","doi-asserted-by":"crossref","unstructured":"Jinjri WM, Keikhosrokiani P, Abdullah NL. Machine learning algorithms for the classification of cardiovascular disease- A comparative study. In: 2021 International Conference on Information Technology (ICIT), 2021, pp.132\u2013138.","DOI":"10.1109\/ICIT52682.2021.9491677"},{"key":"bibr56-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1286-5"},{"key":"bibr57-20552076221150741","volume":"9","author":"Sharma D","year":"2020","journal-title":"International Journal of Engineering and Technology"},{"key":"bibr58-20552076221150741","doi-asserted-by":"publisher","DOI":"10.3390\/app10113956"},{"key":"bibr59-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-020-00851-w"},{"key":"bibr60-20552076221150741","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3043290"},{"key":"bibr61-20552076221150741","unstructured":"Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer \u2026, 2005."},{"key":"bibr62-20552076221150741","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-7-1247-2014"}],"container-title":["DIGITAL HEALTH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076221150741","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20552076221150741","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076221150741","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:52:34Z","timestamp":1777398754000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20552076221150741"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":62,"alternative-id":["10.1177\/20552076221150741"],"URL":"https:\/\/doi.org\/10.1177\/20552076221150741","relation":{},"ISSN":["2055-2076","2055-2076"],"issn-type":[{"value":"2055-2076","type":"print"},{"value":"2055-2076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"article-number":"20552076221150741"}}