Evidence map›Paper›PMID 42057138›Full record

ArticleBMC medical informatics and decision making2026

Predicting adolescent depression: an interpretable machine learning model.

Xingyu Chen, Baoqin Yang, Lu Han, Xinyu Li, Wei Jin, Zhongxuan Xie, Ming Tao, Yanjiang Chen, Yujia Gu, Owusu Mensah Solomon and 2 more

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Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Xingyu Chen *The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310005, China.
Baoqin Yang *The Affiliated Kangning Hospital of Ningbo University, Ningbo, 315201, China.
Lu HanThe Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310005, China.
Xinyu LiThe Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310005, China.
Wei JinThe Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China.
Zhongxuan XieThe Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310005, China.
Ming TaoThe Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China.
Yanjiang ChenHenan University of Chinese Medicine, Henan, 450046, China.
Yujia GuCollege of Nursing, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
Owusu Mensah SolomonCollege of Nursing, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
Lihong LiThe Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China. 20214017@zcmu.edu.cn.
Yehong WeiThe Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China. 20074015@zcmu.edu.cn.

Funding

National College Student Innovation and Entrepreneurship Project of China Grant No. S202410344047"Pioneer" and "Leading Goose" R&D Program of Zhejiang Grant No. 2024C03208Zhejiang Key Research Projects of Traditional Chinese Medicine Grant No. GZY-ZJ-KJ-23017
6 · The paper itself

Abstract

objectivesAdolescent depression is a global mental health problem, increasing the social and economic burden. Using machine learning methods can better predict the risk of depression in adolescents, and provide a reference for the prevention and early intervention of the occurrence of depression in adolescents.

methodsIn this study, we collected various data of 1226 juvenile patients aged 13–25 years, selected the best prediction model from the five machine learning algorithms according to the area under the curve, compared the prediction effect of the five models, selected a model with the best prediction performance, and used the SHAP method for interpretation.

resultsThe XGBoost algorithm has the best predictive performance in distinguishing whether adolescents are at risk for depression. According to the SHapley Additive exPlanations results, the factors most associated with the risk of adolescent depression are sleep factors, family factors, and education level.

conclusionsThe XGBoost-based machine learning classifier can relatively accurately predict the risk of depression in adolescents. This study provides a reference for the prevention and early intervention of the occurrence of depression in adolescents.

Indexed as

DepressionMachine LearningAdolescentBoosting Machine Learning AlgorithmsClassification AlgorithmsFemaleHumansMalePrediction AlgorithmsPredictive Learning ModelsYoung AdultAdolescent depressionMachine learningRisk prediction

Identifiers

PMID42057138
PMCPMC13270717

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.