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

Radwan Qasrawi; Stephanny Vicuna Polo; Rami Abu Khader; Diala Abu Al-Halawa; Sameh Hallaq; Nael Abu Halaweh; Ziad Abdeen

doi:10.3389/fpsyt.2023.1071622

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

Front Psychiatry. 2023 May 26:14:1071622. doi: 10.3389/fpsyt.2023.1071622. eCollection 2023.

Authors

Radwan Qasrawi^{1

2}, Stephanny Vicuna Polo³, Rami Abu Khader³, Diala Abu Al-Halawa⁴, Sameh Hallaq⁵, Nael Abu Halaweh¹, Ziad Abdeen⁴

Affiliations

¹ Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine.
² Department of Computer Engineering, Istinye University, Istanbul, Türkiye.
³ Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine.
⁴ Faculty of Medicine, Al-Quds University, Jerusalem, Palestine.
⁵ Al-Quds Bard College for Arts and Sciences, Al-Quds University, Jerusalem, Palestine.

Abstract

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.

Keywords: cognitive abilities; health; machine learning; mental health; nutrition; prediction; social support.