Cargando…

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

INTRODUCTION: Mental 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,...

Descripción completa

Detalles Bibliográficos
Autores principales: Qasrawi, Radwan, Vicuna Polo, Stephanny, Abu Khader, Rami, Abu Al-Halawa, Diala, Hallaq, Sameh, Abu Halaweh, Nael, Abdeen, Ziad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250653/
https://www.ncbi.nlm.nih.gov/pubmed/37304448
http://dx.doi.org/10.3389/fpsyt.2023.1071622
_version_ 1785055799214604288
author Qasrawi, Radwan
Vicuna Polo, Stephanny
Abu Khader, Rami
Abu Al-Halawa, Diala
Hallaq, Sameh
Abu Halaweh, Nael
Abdeen, Ziad
author_facet Qasrawi, Radwan
Vicuna Polo, Stephanny
Abu Khader, Rami
Abu Al-Halawa, Diala
Hallaq, Sameh
Abu Halaweh, Nael
Abdeen, Ziad
author_sort Qasrawi, Radwan
collection PubMed
description INTRODUCTION: Mental 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. METHODS: This 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. RESULTS: This 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. DISCUSSION: The 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.
format Online
Article
Text
id pubmed-10250653
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-102506532023-06-10 Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments Qasrawi, Radwan Vicuna Polo, Stephanny Abu Khader, Rami Abu Al-Halawa, Diala Hallaq, Sameh Abu Halaweh, Nael Abdeen, Ziad Front Psychiatry Psychiatry INTRODUCTION: Mental 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. METHODS: This 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. RESULTS: This 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. DISCUSSION: The 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. Frontiers Media S.A. 2023-05-26 /pmc/articles/PMC10250653/ /pubmed/37304448 http://dx.doi.org/10.3389/fpsyt.2023.1071622 Text en Copyright © 2023 Qasrawi, Vicuna Polo, Abu Khader, Abu Al-Halawa, Hallaq, Abu Halaweh and Abdeen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Qasrawi, Radwan
Vicuna Polo, Stephanny
Abu Khader, Rami
Abu Al-Halawa, Diala
Hallaq, Sameh
Abu Halaweh, Nael
Abdeen, Ziad
Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
title Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
title_full Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
title_fullStr Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
title_full_unstemmed Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
title_short Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
title_sort machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250653/
https://www.ncbi.nlm.nih.gov/pubmed/37304448
http://dx.doi.org/10.3389/fpsyt.2023.1071622
work_keys_str_mv AT qasrawiradwan machinelearningtechniquesforidentifyingmentalhealthriskfactorassociatedwithschoolchildrencognitiveabilitylivinginpoliticallyviolentenvironments
AT vicunapolostephanny machinelearningtechniquesforidentifyingmentalhealthriskfactorassociatedwithschoolchildrencognitiveabilitylivinginpoliticallyviolentenvironments
AT abukhaderrami machinelearningtechniquesforidentifyingmentalhealthriskfactorassociatedwithschoolchildrencognitiveabilitylivinginpoliticallyviolentenvironments
AT abualhalawadiala machinelearningtechniquesforidentifyingmentalhealthriskfactorassociatedwithschoolchildrencognitiveabilitylivinginpoliticallyviolentenvironments
AT hallaqsameh machinelearningtechniquesforidentifyingmentalhealthriskfactorassociatedwithschoolchildrencognitiveabilitylivinginpoliticallyviolentenvironments
AT abuhalawehnael machinelearningtechniquesforidentifyingmentalhealthriskfactorassociatedwithschoolchildrencognitiveabilitylivinginpoliticallyviolentenvironments
AT abdeenziad machinelearningtechniquesforidentifyingmentalhealthriskfactorassociatedwithschoolchildrencognitiveabilitylivinginpoliticallyviolentenvironments