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.

Publication

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

2025Journal article

datacite.subject.fos	Ciências Médicas::Ciências da Saúde
datacite.subject.fos	Ciências Sociais::Psicologia
datacite.subject.fos	Ciências Sociais::Ciências da Educação
datacite.subject.sdg	03:Saúde de Qualidade
dc.contributor.author	Encarnação, Samuel
dc.contributor.author	Vaz, Paula Marisa Fortunato
dc.contributor.author	Vaz, Filipe J.A.
dc.contributor.author	Fortunato, Álvaro
dc.contributor.author	Monteiro, António M.
dc.date.accessioned	2025-09-08T14:07:03Z
dc.date.available	2025-09-08T14:07:03Z
dc.date.issued	2025
dc.description.abstract	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	We gratefully acknowledge the financial support from The Scientific Board of the University of Porto, Portugal, approved (Identification number: CE18082). We also thank the Public Health Unit of Bragança, a City in the North of Portugal, and the Instituto Politécnico de Bragança (IPB).
dc.identifier.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
dc.identifier.doi	10.1016/j.actpsy.2025.105324
dc.identifier.issn	0001-6918
dc.identifier.uri	http://hdl.handle.net/10198/34744
dc.language.iso	eng
dc.peerreviewed	yes
dc.publisher	Elsevier
dc.rights.uri	http://creativecommons.org/licenses/by/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	por
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.endPage	12
oaire.citation.startPage	1
oaire.citation.title	Acta Psychologica
oaire.citation.volume	259
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyName	Encarnação
person.familyName	Vaz
person.familyName	Monteiro
person.givenName	Samuel
person.givenName	Paula Marisa Fortunato
person.givenName	António M.
person.identifier.ciencia-id	9416-E2F5-E660
person.identifier.ciencia-id	421B-9F32-65C9
person.identifier.ciencia-id	C41C-6CCD-A1F0
person.identifier.orcid	0000-0003-2965-2777
person.identifier.orcid	0000-0001-7678-6781
person.identifier.orcid	0000-0003-4467-1722
person.identifier.rid	K-6545-2015
person.identifier.scopus-author-id	57191541426
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