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,...
Autores principales: | , , , , , , |
---|---|
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 |