Mental illness risk prediction in high school students using artificial neural network.
Mental illness risk prediction in high school students using artificial neural network.
S. Encarnação,P. Vaz,2 Authors,António Miguel de Barros Monteiro
2025 · DOI: 10.1016/j.actpsy.2025.105324
Acta Psychologica · 1 Citations
TLDR
An artificial neural network was conducted to predict the university students' self-reported mental health dimensions and the ANN best predicted the university students' depressive state.
Abstract
INTRODUCTION
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 %).