{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T21:16:09Z","timestamp":1791321369357,"version":"4.3.1"},"reference-count":74,"publisher":"Wiley","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":"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":[[2023,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aim<\/jats:title>\n                    <jats:p>Schizophrenia and bipolar disorder (BD) are critical and high\u2010risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full\u2010text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML can precisely predict results and assist in making clinical decisions\u2010concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.962","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:25:30Z","timestamp":1672219530000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6987-6199","authenticated-orcid":false,"given":"Mahdieh","family":"Montazeri","sequence":"first","affiliation":[{"name":"Department of Health Information Sciences, Faculty of Management and Medical Information Sciences Kerman University of Medical Sciences Kerman Iran"},{"name":"Medical Informatics Research Center, Institute for Futures Studies in Health Kerman University of Medical Sciences Kerman Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitra","family":"Montazeri","sequence":"additional","affiliation":[{"name":"Medical Informatics Research Center, Institute for Futures Studies in Health Kerman University of Medical Sciences Kerman Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5430-3758","authenticated-orcid":false,"given":"Kambiz","family":"Bahaadinbeigy","sequence":"additional","affiliation":[{"name":"Medical Informatics Research Center, Institute for Futures Studies in Health Kerman University of Medical Sciences Kerman Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohadeseh","family":"Montazeri","sequence":"additional","affiliation":[{"name":"Department of Computer, Faculty of Fatimah Kerman Branch Technical and Vocational University Kerman Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Afraz","sequence":"additional","affiliation":[{"name":"Medical Informatics Research Center, Institute for Futures Studies in Health Kerman University of Medical Sciences Kerman Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.56.2.162"},{"key":"e_1_2_14_3_1","unstructured":"HealA.A call for action by World Health Ministers.http:\/\/www.who.int\/entity\/mental_health\/media\/en\/249.pdf(22 August 2015 dla)."},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/epirev\/mxn001"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.64.5.543"},{"key":"e_1_2_14_6_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamapsychiatry.2013.1394"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1001\/archgenpsychiatry.2011.113"},{"key":"e_1_2_14_8_1","unstructured":"AskenMJ GrossmanD ChristensenLW.American Psychiatric Association.Diagnostic and statistical manual of mental disorders. 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