Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments

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dc.authoridvicuna polo, stephanny paola/0000-0002-8308-6801
dc.authoridHallaq, Sameh/0000-0002-5118-5928
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorPolo, Stephanny Vicuna
dc.contributor.authorAbu Khader, Rami
dc.contributor.authorAbu Al-Halawa, Diala
dc.contributor.authorHallaq, Sameh
dc.contributor.authorAbu Halaweh, Nael
dc.contributor.authorAbdeen, Ziad
dc.date.accessioned2024-05-19T14:45:48Z
dc.date.available2024-05-19T14:45:48Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIntroductionMental health and cognitive development are critical aspects of a child's overall well-being; they can be particularly challenging for children living in politically violent environments. Children in conflict areas face a range of stressors, including exposure to violence, insecurity, and displacement, which can have a profound impact on their mental health and cognitive development. MethodsThis study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. ResultsThis study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. DiscussionThe findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being.en_US
dc.identifier.doi10.3389/fpsyt.2023.1071622
dc.identifier.issn1664-0640
dc.identifier.pmid37304448en_US
dc.identifier.scopus2-s2.0-85161437117en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.3389/fpsyt.2023.1071622
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5349
dc.identifier.volume14en_US
dc.identifier.wosWOS:001002375200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherFrontiers Media Saen_US
dc.relation.ispartofFrontiers In Psychiatryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectMental Healthen_US
dc.subjectCognitive Abilitiesen_US
dc.subjectMachine Learningen_US
dc.subjectPredictionen_US
dc.subjectHealthen_US
dc.subjectSocial Supporten_US
dc.subjectNutritionen_US
dc.titleMachine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environmentsen_US
dc.typeArticleen_US

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