Publicação
Mental illness risk prediction in high school students using artificial neural network
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| datacite.subject.sdg | 04:Educação de Qualidade | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| dc.contributor.author | Encarnação, Samuel | |
| dc.contributor.author | Vaz, Paula Fortunato | |
| dc.contributor.author | Vaz, Filipe | |
| dc.contributor.author | Fortunato, Álvaro | |
| dc.contributor.author | Monteiro, António Miguel de Barros | |
| dc.date.accessioned | 2026-03-24T17:23:05Z | |
| dc.date.available | 2026-03-24T17:23:05Z | |
| dc.date.issued | 2025-09 | |
| dc.description.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 %). | eng |
| dc.description.sponsorship | CE18082 | |
| dc.identifier.doi | 10.1016/j.actpsy.2025.105324 | |
| dc.identifier.issn | 0001-6918 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/28532 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier | |
| dc.relation.ispartof | Acta Psychologica | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Mental illness | |
| dc.subject | Psychological distress | |
| dc.subject | Deep learning | |
| dc.subject | Quality of life | |
| dc.subject | Well-being | |
| dc.title | Mental illness risk prediction in high school students using artificial neural network | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.startPage | 105324 | |
| oaire.citation.title | Acta Psychologica | |
| oaire.citation.volume | 259 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Fortunato | |
| person.givenName | Álvaro | |
| person.identifier.orcid | 0000-0002-9965-309X | |
| relation.isAuthorOfPublication | a6a3ff07-e31a-4885-ba58-723e97ef167e | |
| relation.isAuthorOfPublication.latestForDiscovery | a6a3ff07-e31a-4885-ba58-723e97ef167e |