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Mental illness risk prediction in high school students using artificial neural network
Samuel Encarnação1, Paula Fortunato Vaz2, Filipe Vaz3
1Department of Physical Activity and Sport Sciences, Universidad Autónoma de Madrid (UAM), Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain; Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal; Live Well Research Centre for Active Living and Wellbeing, Instituto Politécnico de Bragança, Portugal; CI ISE, Instituto Superior de Ciências Educativas do Douro, 4560 547 Penafiel, Portugal.
Artificial neural networks accurately predict university students' depressive states, aligning with global health goals. This AI approach aids in understanding and addressing student mental health challenges effectively.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Public Health
Background:
- Student mental health is crucial for achieving the UN's 2030 Sustainable Development Goals, specifically Goal 3.
- The study addresses the need for reliable methods to assess and predict mental well-being among university students.
Purpose of the Study:
- To develop and apply an artificial neural network (ANN) model for predicting self-reported mental health dimensions in university students.
- To evaluate the ANN's predictive accuracy for various aspects of student mental health.
Main Methods:
- A cross-sectional, observational study involving 2050 university students aged 18-30 years.
- Collection of sociodemographic and health state data.
- Application of artificial neural network algorithms for predictive modeling.
Main Results:
- The ANN model achieved high accuracy in predicting students' depressive states, with a 73% cross-validation accuracy.
- Predictive accuracy for overall mental health self-perception was 60%, and for lack of interest in daily activities (ADLs) was 67%.
Conclusions:
- Artificial neural networks demonstrate significant potential in accurately predicting university students' depressive states.
- The findings highlight the utility of AI in mental health research and support for student well-being initiatives.
