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Occupational Mental Health: An Investigation of Risk Indicators Using Interpretable Machine Learning Techniques.

André Luis Schneider1, Juliana Sampaio do Carmo, Érick Oliveira Rodrigues

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Interpretable machine learning accurately identifies work-related mental health risks, revealing key factors like work removal and high-risk jobs for early intervention strategies.

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SHAP analysisclassification modelsmachine learningmental disordersoccupational health

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Area of Science:

  • Occupational Health
  • Data Science
  • Public Health

Background:

  • Work-related mental health issues pose a significant public health challenge.
  • Early identification of risk factors is crucial for effective intervention.

Purpose of the Study:

  • To apply interpretable machine learning (ML) models to identify key factors influencing work-related mental health cases.
  • To support the development of early intervention strategies for mental health in the workplace.

Main Methods:

  • Utilized 1117 records from Brazil's Notifiable Diseases Information System (2007-2022).
  • Developed five ML models to classify mental health cases as mild or severe.
  • Employed SHAP (SHapley Additive exPlanations) analysis for predictor interpretation.

Main Results:

  • Decision tree model achieved 82.9% accuracy; Support Vector Machine reached 82.0% accuracy.
  • Identified key determinants including work removal, protective measures, and regional factors.
  • High-risk occupations identified: energy/water operators, legal professionals, and engineers.

Conclusions:

  • Interpretable ML models demonstrate effectiveness in predicting mental health outcomes.
  • Revealed actionable sociodemographic and occupational risk factors for targeted interventions.
  • Findings support proactive strategies to mitigate work-related mental health issues.