{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T05:57:10Z","timestamp":1784872630228,"version":"3.55.0"},"reference-count":62,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To provide an available diagnostic model for diagnosing coronary atherosclerotic heart disease to provide an auxiliary function for doctors, we proposed a new evolutionary classification model in this paper. The core of the prediction model is a kernel extreme learning machine (KELM) optimized by an improved salp swarm algorithm (SSA). To get a better subset of parameters and features, the space transformation mechanism is introduced in the optimization core to improve SSA for obtaining an optimal KELM model. The KELM model for the diagnosis of coronary atherosclerotic heart disease (STSSA-KELM) is developed based on the optimal parameters and a subset of features. In the experiment, STSSA-KELM is compared with some widely adopted machine learning methods (MLM) in coronary atherosclerotic heart disease prediction. The experimental results show that STSSA-KELM can realize excellent classification performance and more robust stability under four indications. We also compare the convergence of STSSA-KELM with other MLM; the STSSA-KELM model has demonstrated a higher classification performance. Therefore, the STSSA-KELM model can effectively help doctors to diagnose coronary heart disease.<\/jats:p>","DOI":"10.3390\/sym12101651","type":"journal-article","created":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T07:23:22Z","timestamp":1602919402000},"page":"1651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm"],"prefix":"10.3390","volume":"12","author":[{"given":"Wenming","family":"He","sequence":"first","affiliation":[{"name":"Department of Cardiology, the Affiliated Hospital of Medical School; Ningbo University, Ningbo 315020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqing","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Cardiology, the Affiliated Hospital of Medical School; Ningbo University, Ningbo 315020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoxuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, the Affiliated Hospital of Medical School; Ningbo University, Ningbo 315020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjing","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7714-9693","authenticated-orcid":false,"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1177\/2047487315587402","article-title":"National prevalence of coronary heart disease and its relationship with human development index: A systematic review","volume":"23","author":"Zhu","year":"2016","journal-title":"Eur. 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