Identifying the risk of depression in a large sample of adolescents

An artificial neural network based on random forest

Dados Bibliográficos

ID21444360
AutoresYue Zhou (0009-0009-2070-9715, Department of Maternal, Child and Adolescent Health, School of Public Health Lanzhou University Lanzhou Gansu China), Xuelian Zhang (0000-0003-4715-1634, Department of Nosocomial Infection Control, Division of Medical Administration The Third People's Hospital of Gansu Province Lanzhou Gansu China), Jian Gong (0000-0003-1512-4762, Department of Maternal, Child and Adolescent Health, School of Public Health Lanzhou University Lanzhou Gansu China), Jian Ping Gong (0000-0003-2228-2750, Gansu Provincial Maternal and Child Health Hospital), Tingwei Wang (0009-0001-0811-8906, Department of Maternal, Child and Adolescent Health, School of Public Health Lanzhou University Lanzhou Gansu China), Linlin Gong (Department of Maternal, Child and Adolescent Health, School of Public Health Lanzhou University Lanzhou Gansu China), Lin-Lin Gong (Gansu Provincial Maternal and Child Health Hospital), Kaida Li (School of Computer Science and Technology East China Normal University Shanghai China), Yanni Wang (0000-0003-0747-173X, Department of Maternal, Child and Adolescent Health, School of Public Health Lanzhou University Lanzhou Gansu China, autor correspondente)
Ano2024
Volume96
Fascículo7
Páginas1485-1497
Data de publicação2024-10-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoJournal of Adolescence (JOURNAL)
Identificadores do periódicoISSN: 0140-1971 • E-ISSN: 1095-9254
EditoraWiley (PUBLISHER • GB)
DOI10.1002/jad.12357
PMID38837218
OpenAlexW4399330309
IdiomaEN
Citações recebidas1
Referências citadas67

BACKGROUND: This study aims to develop an artificial neural network (ANN) prediction model incorporating random forest (RF) screening ability for predicting the risk of depression in adolescents and identifies key risk factors to provide a new approach for primary care screening of depression among adolescents. METHODS: The data were from a large cross-sectional study conducted in China from July to September 2021, enrolling 8635 adolescents aged 10-17 with their parents. We used the Patient health questionnaire (PHQ-9) to rate adolescent depression symptoms, using scales and single-item questions to collect demographic information and other variables. Initial model variables screening used the RF importance assessment, followed by building prediction model using the screened variables through the ANN. RESULTS: The rate of depression symptoms in adolescents was 24.6%, and the depression risk prediction model was built based on 70% of the training set and 30% of the test set. Ten variables were included in the final prediction model with a model accuracy of 85.03%, AUC of 0.892, specificity of 89.79%, and sensitivity of 70.81%. The top 10 significant factors of depression risk were adolescent rumination, adolescent self-esteem, adolescent mobile phone addiction, peer victimization, care in parenting styles, overprotection in parenting styles, academic pressure, conflict in parent-child relationship, parental rumination, and relationship between parents. CONCLUSIONS: The ANN model based on the RF effectively identifies depression risk in adolescents and provides a methodological reference for large-scale primary screening. Cross-sectional studies and single-item scales limit further improvements in model accuracy.

Depression (economics) · Psychiatry · Rumination · Child and Adolescent Psychosocial and Emotional Development · Clinical Psychology · Digital Mental Health Interventions · Mental Health via Writing · Psychology