Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest.
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...
| Publicado en: | Journal of Adolescence Vol. 96; no. 7; pp. 1485 - 1498 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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Wiley-Blackwell
Oct2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=180109722&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 180109722 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 01401971 JAA jtl: Journal of Adolescence issn: 01401971 maglogo: N pubinfo: dt: Oct2024 vid: 96 iid: 7 pid: 480 pub: Wiley-Blackwell artinfo: ui: 180109722 10.1002/jad.12357 ppf: 1485 ppct: 13 formats: tig: atl: Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest. aug: au: Zhou, Yue Zhang, Xuelian Gong, Jian Wang, Tingwei Gong, Linlin Li, Kaida Wang, Yanni affil: Department of Maternal, Child and Adolescent Health, School of Public Health, Lanzhou University, Lanzhou Gansu,, China Department of Nosocomial Infection Control, Division of Medical Administration, The Third People's Hospital of Gansu Province, Lanzhou Gansu,, China School of Computer Science and Technology, East China Normal University, Shanghai, China su: Depression in adolescence Self-esteem in adolescence Rumination (Cognition) Artificial neural networks Random forest algorithms sug: subj: Depression in adolescence Self-esteem in adolescence Rumination (Cognition) Artificial neural networks Random forest algorithms keyword: adolescents artificial neural network depression prediction model primary screening random forest adolescents artificial neural network depression prediction model primary screening random forest ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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