Mental illness risk prediction in high school students using artificial neural network

Encarnação, Samuel; Vaz, Paula Marisa Fortunato; Vaz, Filipe J.A.; Fortunato, Álvaro; Monteiro, António M.

http://hdl.handle.net/10198/34744

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Authors

Encarnação, Samuel

Vaz, Paula Marisa Fortunato

Vaz, Filipe J.A.

Fortunato, Álvaro

Monteiro, António M.

Abstract(s)

The sustainable development goals of the United Nations 2030 agenda, goal number 3 – Good health and well-being- align with student mental health. Objective: To conduct an artificial neural network (ANN) to predict the students' self-reported mental health dimensions. Methods: A cross-sectional and observational study enrolling sociodemographic and health state data from 2050 university students aged (18–30 years). Results: The best algorithm's result was by predicting the students' depressive state with 97 % accuracy (weighted average = [precision = 0.79 %, recall = 0.79 %, F-1 score 0 0.79 %, cross-validation (73 %)]), while dimensions such overall mental health self-perception (validation accuracy = 60 %) and lack of interest in performing their activities of daily living [(ADLs), validation accuracy = 67 %], presented inferior predictions. Conclusions: The ANN best predicted the university students' depressive state (73 %).

Keywords

Mental illness Psychological distress Deep learning Quality of life Well-being

URI

http://hdl.handle.net/10198/34744

Citation

Encarnação, Samuel; Vaz, Paula Marisa Fortunato; Vaz, Filipe J.A.; Fortunato, Álvaro; Monteiro, António M. (2025). Mental illness risk prediction in high school students using artificial neural network. Acta Psychologica. ISSN 0001-6918. 259, p. 1-12