Anxiety in young people: Analysis from a machine learning model.

Autor: Tabares Tabares M; Grupo Telesalud, Universidad de Caldas, Colombia. Electronic address: marcela.tabares@ucaldas.edu.co., Vélez Álvarez C; Grupo Promoción de la Salud y Prevención de la Enfermedad, Universidad de Caldas, Colombia. Electronic address: consuelo.velez@ucaldas.edu.co., Bernal Salcedo J; Universidad de Caldas, Colombia. Electronic address: joshua.bernals@autonoma.edu.co., Murillo Rendón S; Grupo Inteligencia Artificial, Universidad de Caldas, Colombia; Grupo Ingeniería de Software, Universidad Autónoma de Manizales, Colombia. Electronic address: santiago.murillo@ucaldas.edu.co.
Jazyk: angličtina
Zdroj: Acta psychologica [Acta Psychol (Amst)] 2024 Aug; Vol. 248, pp. 104410. Date of Electronic Publication: 2024 Jul 20.
DOI: 10.1016/j.actpsy.2024.104410
Abstrakt: The study addresses the detection of anxiety symptoms in young people using artificial intelligence models. Questionnaires such as the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7) are used to collect data, with a focus on early detection of anxiety. Three machine learning models are employed: Support Vector Machine (SVM), K Nearest Neighbors (KNN), and Random Forest (RF), with cross-validation to assess their effectiveness. Results show that the RF model is the most efficient, with an accuracy of 91 %, surpassing previous studies. Significant predictors of anxiety are identified, such as parental education level, alcohol consumption, and social security affiliation. A relationship is observed between anxiety and personal and family history of mental illness, as well as with characteristics external to the model, such as family and personal history of depression. The analysis of the results highlights the importance of considering not only clinical but also social and family aspects in mental health interventions. It is suggested that the sample size be expanded in future studies to improve the robustness of the model. In summary, the study demonstrates the usefulness of artificial intelligence in the early detection of anxiety in young people and highlights the relevance of addressing multidimensional factors in the assessment and treatment of this condition.
Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest or commercial relationships with the project's funders.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
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