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Machine learning-based predictive modelling of mental health in Rwandan Youth.

Fauste Ndikumana1,2, Josias Izabayo3, Joseph Kalisa3

  • 1African Center of Excellence in Data Sciences, University of Rwanda, Kigali, Rwanda. nfauste@gmail.com.

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|May 9, 2025
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Summary
This summary is machine-generated.

Machine learning accurately predicts mental health vulnerability in Rwandan youth. Traumatic events, violence, heavy drinking, and family history are key risk factors, highlighting the need for targeted interventions.

Keywords:
Machine learningMental healthPredictionRwandaYouth

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Area of Science:

  • Global Mental Health
  • Computational Psychiatry
  • Public Health Informatics

Background:

  • Mental disorders represent a substantial global health burden, disproportionately affecting low- and middle-income countries.
  • Rwanda exhibits high mental disorder prevalence, particularly among survivors of the 1994 genocide against Tutsi.
  • Machine learning (ML) shows potential for identifying complex patterns in mental health data, yet its application in Rwanda is limited.

Purpose of the Study:

  • To apply machine learning techniques to predict mental health vulnerability among Rwandan youth.
  • To identify significant risk factors associated with mental health disorders and comorbidity in this population.
  • To explore the utility of ML in understanding mental health determinants in a post-conflict setting.

Main Methods:

  • Utilized a dataset of 5221 Rwandan youth from the Rwanda Biomedical Center's mental health cross-sectional study.
  • Employed four machine learning models: logistic regression, Support Vector Machine, Random Forest, and Gradient Boosting.
  • Evaluated model performance for predicting mental health vulnerability and mental disorder comorbidity.

Main Results:

  • The Random Forest model demonstrated the highest accuracy (88.8%) in predicting mental health vulnerability.
  • The Random Forest model achieved 75% accuracy in predicting mental disorder comorbidity.
  • Significant risk factors identified include exposure to traumatic events and violence, heavy drinking, and family history of mental health issues.

Conclusions:

  • Machine learning effectively predicts factors associated with mental health vulnerability and comorbidity in Rwandan youth.
  • Social factors (trauma, violence) and biological factors (family history) significantly contribute to mental health disorders.
  • Mental health interventions and policies in Rwanda should prioritize youth experiencing social hardship, particularly those exposed to violence and trauma.