{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T02:41:21Z","timestamp":1789094481455,"version":"build-2803163510"},"reference-count":46,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstrac<\/jats:title><jats:sec><jats:title>Background and Aims<\/jats:title><jats:p>Stroke is one of the most common causes of death worldwide, leading to numerous complications and significantly diminishing the quality of life for those affected. The purpose of this study is to systematically review published papers on stroke prediction using machine learning algorithms and introduce the most efficient machine learning algorithms and compare their performance. The papers have published in period from 2019 to August 2023.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The authors conducted a systematic search in PubMed, Scopus, Web of Science, and IEEE using the keywords \u201cArtificial Intelligence,\u201d \u201cPredictive Modeling,\u201d \u201cMachine Learning,\u201d \u201cStroke,\u201d and \u201cCerebrovascular Accident\u201d from 2019 to August 2023.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Twenty articles were included based on the inclusion criteria. The Random Forest (RF) algorithm was introduced as the best and most efficient stroke ML algorithm in 25% of the articles (<jats:italic>n<\/jats:italic>\u2009=\u20095). In addition, in other articles, Support Vector Machines (SVM), Stacking and XGBOOST, DSGD, COX&amp; GBT, ANN, NB, and RXLM algorithms were introduced as the best and most efficient ML algorithms in stroke prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This research has shown a rapid increase in using ML algorithms to predict stroke, with significant improvements in model accuracy in recent years. However, no model has reached 100% accuracy or is entirely error\u2010free. Variations in algorithm efficiency and accuracy stem from differences in sample sizes, datasets, and data types. Further studies should focus on consistent datasets, sample sizes, and data types for more reliable outcomes.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hsr2.70062","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T10:40:47Z","timestamp":1727779247000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["The most efficient machine learning algorithms in stroke prediction: A systematic review"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0939-7983","authenticated-orcid":false,"given":"Farkhondeh","family":"Asadi","sequence":"first","affiliation":[{"name":"Department of Health Information Technology and Management School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2750-3826","authenticated-orcid":false,"given":"Milad","family":"Rahimi","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology Urmia University of Medical Sciences Urmia Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2208-953X","authenticated-orcid":false,"given":"Amir Hossein","family":"Daeechini","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology and Management School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5873-1856","authenticated-orcid":false,"given":"Atefeh","family":"Paghe","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology and Management School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21207609"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/NPT.0000000000000363"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-023-01735-1"},{"key":"e_1_2_12_5_1","first-page":"6","article-title":"Stroke prediction using machine learning algorithms","volume":"8","author":"Harshitha KV","year":"2021","journal-title":"Int J Innov Res Engineer Manag"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2023.1039794"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106176"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0000000000000375"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2022.100116"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijs.12371"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.30812\/matrik.v21i3.1666"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59115-y"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2018.03.521"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines11041138"},{"issue":"12","key":"e_1_2_12_15_1","first-page":"2222","article-title":"What is machine learning? 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