Enhancing Mental Health Predictions: A Gradient Boosted Model for Sri Lankan Camp Refugees

Indranil Sahoo (), Elizabeth Amona, Miriam Kuttikat and David Chan
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Indranil Sahoo: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
Elizabeth Amona: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA
Miriam Kuttikat: School of Social Work, Virginia Commonwealth University, Richmond, VA 23284, USA
David Chan: Department of Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284, USA

Social Sciences, 2024, vol. 13, issue 5, 1-14

Abstract: This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in camps, and psychological variables. In quantitative mental health research, linear regression serves as a conventional approach for assessing the influence of diverse factors on mental health outcomes. However, this method fails to accommodate non-linear relationships between mental health variables and predictors and relies on stringent model assumptions that often do not align with real-world conditions. This study introduces a model-agnostic, advanced machine learning/artificial intelligence (ML/AI) technique, glmboost, as a viable alternative to linear regression. The glmboost algorithm is capable of fitting non-linear prediction models while also conducting variable selection. Moreover, the coefficients obtained from the glmboost model retain the same interpretability as those derived from linear regression. While the glmboost model identifies several key factors including post-migration living difficulties, post-traumatic stress disorder, difficulty in sleeping, poor family functioning, and lower informal support from families as markers of declining mental well-being among the Sri Lankan refugees, the linear regression overlooks vital predictors such as family functioning and family support, highlighting the importance of utilizing advanced ML/AI techniques like glmboost to comprehensively capture complex relationships between predictor variables and mental health outcomes among refugee populations. Thus, by introducing a novel, data-driven approach to mental health risk assessment, this study paves the way for more precise and efficient analyses and interventions in refugee settings, with the potential for improved resource allocation and personalized support, thus revolutionizing mental health service delivery in challenging environments. Additionally, it contributes to the academic discussion on refugee mental health while emphasizing the pivotal role of advanced data analytics in addressing complex health issues within humanitarian contexts.

Keywords: refugee mental health; gradient boosted model; Sri Lankan camps; predictive analytics; risk assessment; humanitarian data science (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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