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Single classifier vs. ensemble machine learning approaches for mental health prediction.

Jetli Chung1, Jason Teo2,3

  • 1Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.

Brain Informatics
|January 3, 2023
PubMed
Summary

Machine learning models show promise for early mental health problem prediction. Gradient Boosting achieved the highest accuracy at 88.80%, indicating a viable automated approach for mental health professionals.

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Area of Science:

  • Computational psychiatry
  • Machine learning in healthcare

Background:

  • Early prediction of mental health issues is crucial for timely intervention.
  • Machine learning offers a promising avenue for automated mental health problem detection.

Purpose of the Study:

  • To empirically evaluate various machine learning algorithms for mental health problem classification.
  • To compare single classifier and ensemble machine learning approaches.

Main Methods:

  • Investigated algorithms: Logistic Regression, Gradient Boosting, Neural Networks, K-Nearest Neighbours, Support Vector Machine, Extreme Gradient Boosting, Deep Neural Networks, and an ensemble approach.
  • Utilized a dataset from Open Sourcing Mental Illness (OSMI) survey responses.
  • Evaluated performance based on classification accuracy.

Main Results:

  • Gradient Boosting achieved the highest accuracy (88.80%).
  • Neural Networks (88.00%), Extreme Gradient Boosting (87.20%), and Deep Neural Networks (86.40%) also showed strong performance.
  • All investigated machine learning approaches exceeded 80% accuracy.

Conclusions:

  • Gradient Boosting demonstrates superior performance for this mental health prediction task.
  • Machine learning approaches show significant potential for automated clinical diagnosis in mental health.
  • These findings support the integration of machine learning tools for mental health professionals.