SEARCHCROSS-SECTIONALpsychiatry, pediatrics

cross-sectional·psychiatry, pediatrics, public health, epidemiology, research methods·PMC10250653

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

Frontiers in Psychiatry · 7 authors, 5 centres

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TL;DR

The analysis found significant associations between low cognitive ability and factors like maltreatment, exposure to political violence, PTSD, depression, and certain socio-demographic characteristics.

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The ML models identified a cluster of risk factors, with maltreatment being a key predictor, followed by PTSD, smoking, and suicide attempts. These factors were not significant in a comparative logistic regression model, suggesting ML can uncover non-linear relationships. Significant associations were also found between low cognitive ability and being male, urban residence, lower parental education, and specific mental health symptoms. The findings highlight the potential of ML to identify complex risk patterns for cognitive development in conflict-affected children, informing the need for targeted interventions.

STUDY TYPE

cross-sectional

SPECIALTY

psychiatry

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AI pipeline

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ORIGINAL PAPER

PMC10250653

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

Frontiers in Psychiatry

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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.

CITATION EXPORT

Radwan Qasrawi, Stephanny Vicuna Polo, Rami Abu Khader et al.. Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments. Frontiers in Psychiatry. (2023). https://doi.org/10.3389/fpsyt.2023.1071622

AUTHORS

7 · 5 CENTRES

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

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