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Machine learning models accurately predict negative mental well-being using factors like physical activity and GPA. These AI tools can enhance early detection and support for students.

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Area of Science:

  • Computational psychology
  • Artificial intelligence in mental health
  • Machine learning for well-being assessment

Background:

  • The COVID-19 pandemic heightened the need for effective mental well-being assessment.
  • Machine learning (ML) and artificial intelligence (AI) offer potential for early detection of negative psychological states.

Purpose of the Study:

  • To model mental well-being using various machine learning algorithms.
  • To evaluate the performance of ML algorithms in identifying negative psychological well-being.

Main Methods:

  • Utilized data from a large, multi-site cross-sectional survey of 17 universities in Southeast Asia.
  • Applied and compared the performance of multiple machine learning algorithms: generalized linear models, k-nearest neighbor, naïve Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting.

Main Results:

  • Random Forest and adaptive boosting algorithms demonstrated the highest accuracy in identifying negative mental well-being traits.
  • Key predictors of poor mental well-being included weekly sports activity, body mass index, grade point average (GPA), sedentary hours, and age.

Conclusions:

  • Findings suggest ML models can modernize mental well-being assessment and monitoring at individual and university levels.
  • Recommendations for future work and cost-effective support strategies are discussed.