A carregar...
Publicação
Mental illness risk prediction in high school students using artificial neural network
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.96 MB | Adobe PDF |
Orientador(es)
Resumo(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 %).
Descrição
Palavras-chave
Mental illness Psychological distress Deep learning Quality of life Well-being
Contexto Educativo
Citação
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
Editora
Elsevier