Determining the probability of juvenile delinquency by using support vector machines and designing a clinical decision support system

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dc.authoridSARI, SEDA/0000-0003-4793-0662
dc.authoriducuz, ilknur/0000-0003-1986-4688
dc.authoridÖzcan, Özlem/0000-0003-3267-2648;
dc.authorwosidSARI, SEDA/AAB-3325-2021
dc.authorwosidCİCEK, AYLA UZUN/R-5022-2018
dc.authorwosiducuz, ilknur/ABB-2349-2020
dc.authorwosidÖzcan, Özlem/ABH-9167-2020
dc.authorwosidARI, ALİ/ABH-1602-2020
dc.contributor.authorUcuz, Ilknur
dc.contributor.authorCicek, Ayla Uzun
dc.contributor.authorAri, Ali
dc.contributor.authorOzcan, Ozlem Ozel
dc.contributor.authorSari, Seda Aybuke
dc.date.accessioned2024-08-04T20:48:44Z
dc.date.available2024-08-04T20:48:44Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIt is a known fact that individuals who engaged in delinquent behavior in childhood are more probable to carry on similar behavior in adulthood. If the factors that lead children to involve in delinquency are defined, the risk of dragging children into crime can be detected before they are involved in crime and delinquency can be prevented with appropriate preventive rehabilitation programs, in the early period. However, given that delinquent behavior occurs under the influence of multiple conditions and factors rather than a single risk factor; the need for diagnostic tools to evaluate multiple factors together is obvious. Artificial intelligence-based clinical decision support systems have already been used in the field of psychiatry as well as many other fields of medicine. In this study, we assume that thanks to artificial intelligence-based clinical decision support systems, children and adolescents at risk can be detected before the criminal behavior occurs by addressing certain factors. In this way, we anticipate that it can provide psychiatrists and other experts in the field.en_US
dc.identifier.doi10.1016/j.mehy.2020.110118
dc.identifier.issn0306-9877
dc.identifier.issn1532-2777
dc.identifier.pmid32721810en_US
dc.identifier.scopus2-s2.0-85088370458en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.mehy.2020.110118
dc.identifier.urihttps://hdl.handle.net/11616/99422
dc.identifier.volume143en_US
dc.identifier.wosWOS:000577511800107en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherChurchill Livingstoneen_US
dc.relation.ispartofMedical Hypothesesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCallous-Unemotional Traitsen_US
dc.subjectAdvancing Paternal Ageen_US
dc.subjectPsychiatric-Disordersen_US
dc.subjectConduct Problemsen_US
dc.subjectSubstance Useen_US
dc.subjectAssociationen_US
dc.subjectPrevalenceen_US
dc.subjectPredictionen_US
dc.subjectYouthen_US
dc.subjectRisken_US
dc.titleDetermining the probability of juvenile delinquency by using support vector machines and designing a clinical decision support systemen_US
dc.typeArticleen_US

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