{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:19:53Z","timestamp":1778757593317,"version":"3.51.4"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13198-025-03034-6","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T08:39:32Z","timestamp":1762504772000},"page":"1272-1283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing mental health diagnosis: a comparative analysis of machine learning approaches"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6577-0653","authenticated-orcid":false,"given":"S.","family":"Regilan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L. K.","family":"Hema","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Kadhiravan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Jenitha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"3034_CR1","doi-asserted-by":"crossref","unstructured":"Dawson-Squibb JJ, Davids EL, Viljoen M, Rice K, Stein DJ (2023) The WHO international classification of diseases 11th revision (ICD-11). In: handbook of clinical child psychology: integrating theory and research into practice, pp. 53\u201378. Springer International Publishing, Cham","DOI":"10.1007\/978-3-031-24926-6_4"},{"key":"3034_CR2","doi-asserted-by":"crossref","unstructured":"First MB (2023) Psychiatric classification. In: Tasman\u2019s psychiatry, pp. 1\u201327. Springer International Publishing, Cham","DOI":"10.1007\/978-3-030-42825-9_91-1"},{"key":"3034_CR3","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2022-073591","author":"FS Goes","year":"2023","unstructured":"Goes FS (2023) Diagnosis and management of bipolar disorders. BMJ. https:\/\/doi.org\/10.1136\/bmj-2022-073591","journal-title":"BMJ"},{"key":"3034_CR4","doi-asserted-by":"publisher","DOI":"10.1111\/inm.13114","author":"O Higgins","year":"2023","unstructured":"Higgins O, Short BL, Chalup SK, Wilson RL (2023) Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: an integrative review. Int J Ment Health Nurs. https:\/\/doi.org\/10.1111\/inm.13114","journal-title":"Int J Ment Health Nurs"},{"issue":"7","key":"3034_CR5","doi-asserted-by":"publisher","first-page":"496","DOI":"10.5771\/0943-7444-2022-7-496","volume":"49","author":"Yi Hong","year":"2023","unstructured":"Hong Yi, Zeng ML (2023) International classification of diseases (ICD). Ko Knowl Organ 49(7):496\u2013528","journal-title":"Ko Knowl Organ"},{"issue":"1","key":"3034_CR6","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s12665-022-10723-z","volume":"82","author":"Y Huan","year":"2023","unstructured":"Huan Y, Song L, Khan U, Zhang B (2023) Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China. Environ Earth Sci 82(1):35","journal-title":"Environ Earth Sci"},{"issue":"3","key":"3034_CR7","doi-asserted-by":"publisher","first-page":"285","DOI":"10.3390\/healthcare11030285","volume":"11","author":"NK Iyortsuun","year":"2023","unstructured":"Iyortsuun NK, Kim S-H, Jhon M, Yang H-J, Pant S (2023) A review of machine learning and deep learning approaches on mental health diagnosis. Healthcare 11(3):285","journal-title":"Healthcare"},{"key":"3034_CR8","doi-asserted-by":"publisher","unstructured":"Karbalaeipour H, Damari S, Zolfagharnasab MH, Haghdadi A (2023) A collection of 120 psychology patients with 17 essential symptoms to diagnose mania bipolar disorder, depressive bipolar disorder, major depressive disorder, and normal individuals. Harvard dataverse. https:\/\/doi.org\/10.7910\/DVN\/0FNET5","DOI":"10.7910\/DVN\/0FNET5"},{"issue":"3","key":"3034_CR9","doi-asserted-by":"publisher","DOI":"10.2196\/24870","volume":"23","author":"J Kim","year":"2021","unstructured":"Kim J, Lee D, Park E (2021) Machine learning for mental health in social media: bibliometric study. J Med Internet Res 23(3):e24870","journal-title":"J Med Internet Res"},{"key":"3034_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104278","volume":"138","author":"I Moura","year":"2023","unstructured":"Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F (2023) Digital phenotyping of mental health using multimodal sensing of multiple situations of interest: a systematic literature review. J Biomed Inform 138:104278","journal-title":"J Biomed Inform"},{"key":"3034_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3304236","author":"C Nash","year":"2023","unstructured":"Nash C, Nair R, Naqvi SM (2023) Machine learning in ADHD and depression mental health diagnosis: a survey. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2023.3304236","journal-title":"IEEE Access"},{"key":"3034_CR12","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s001270050175","volume":"34","author":"G Parker","year":"1999","unstructured":"Parker G, Mahendran R, Yeo SG, Loh MI, Jorm AF (1999) Diagnosis and treatment of mental disorders: a survey of Singapore mental health professionals. Soc Psychiatry Psychiatr Epidemiol 34:555\u2013563","journal-title":"Soc Psychiatry Psychiatr Epidemiol"},{"key":"3034_CR13","doi-asserted-by":"publisher","first-page":"183952","DOI":"10.1109\/ACCESS.2020.3029154","volume":"8","author":"A Rahman","year":"2020","unstructured":"Rahman A, Rohizah KO, Noah SAM, Danuri MSNM, Al-Garadi MA (2020) Application of machine learning methods in mental health detection: a systematic review. IEEE Access 8:183952\u2013183964","journal-title":"IEEE Access"},{"key":"3034_CR14","doi-asserted-by":"publisher","first-page":"1066317","DOI":"10.3389\/fpsyg.2022.1066317","volume":"13","author":"E Sajno","year":"2023","unstructured":"Sajno E, Bartolotta S, Tuena C, Cipresso P, Pedroli E, Riva G (2023) Machine learning in biosignals processing for mental health: a narrative review. Front Psychol 13:1066317","journal-title":"Front Psychol"},{"issue":"5","key":"3034_CR15","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1002\/jclp.23254","volume":"78","author":"KW Samuelson","year":"2022","unstructured":"Samuelson KW, Dixon K, Jordan JT, Powers T, Sonderman S, Brickman S (2022) Mental health and resilience during the coronavirus pandemic: a machine learning approach. J Clin Psychol 78(5):821\u2013846","journal-title":"J Clin Psychol"},{"issue":"5","key":"3034_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3398069","volume":"27","author":"A Thieme","year":"2020","unstructured":"Thieme A, Belgrave D, Doherty G (2020) Machine learning in mental health: a systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Trans Comput Hum Interact (TOCHI) 27(5):1\u201353","journal-title":"ACM Trans Comput Hum Interact (TOCHI)"},{"key":"3034_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102665","volume":"83","author":"S Zhang","year":"2023","unstructured":"Zhang S, Wang J, Yu S, Wang R, Han J, Zhao S, Liu T, Lv J (2023) An explainable deep learning framework for characterizing and interpreting human brain states. Med Image Anal 83:102665","journal-title":"Med Image Anal"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-025-03034-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-025-03034-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-025-03034-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:34:54Z","timestamp":1778754894000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-025-03034-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":17,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["3034"],"URL":"https:\/\/doi.org\/10.1007\/s13198-025-03034-6","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]},"assertion":[{"value":"18 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal participants"}}]}}