Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
Human Genetics01:28

Human Genetics

Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Exosome-Associated Gene Network and the Role of SPP1 in Herpes Stromal Keratitis and the Therapeutic Modulation by Ursolic Acid.

Investigative ophthalmology & visual science·2026
Same author

Effects of Different Sowing Methods on Winter Rapeseed (<i>Brassica rapa</i> L.) Growth and Soil Properties in Saline-Alkali Land.

Plants (Basel, Switzerland)·2026
Same author

Gender differences in mental health profiles among Chinese adolescents: a multiple-group latent profile analysis.

BMC psychology·2026
Same author

Hspa1b attenuates hypoxia/reoxygenation-induced cardiomyocyte injury through dual suppression of P53-driven apoptotic and ferroptotic pathways.

Cell stress & chaperones·2026
Same author

Predictive role of tear metabolomics in delirium during anesthesia emergence and postoperative period in elderly patients after abdominal surgery.

Frontiers in molecular biosciences·2026
Same author

Plant caspase-like proteins: from function identification to application in winter rapeseed genetic breeding.

Frontiers in plant science·2026

Related Experiment Video

Updated: Jun 5, 2026

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression
07:58

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Published on: February 24, 2023

Machine learning-based predictive factor analysis of depression among Chinese adolescents.

Jichang Guo1, Yanpei Pan2, Tingting Fan1

  • 1School of Education Science, Minzu Normal University of Xingyi, Xingyi, China.

Frontiers in Psychiatry
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning effectively predicts adolescent depression risk in China, identifying personality traits like neuroticism and proactive change as key factors. Targeted interventions can help mitigate vulnerability.

Keywords:
adolescent depressionmachine learningneuroticismpersonal growth initiativepredictive factors

Related Experiment Videos

Last Updated: Jun 5, 2026

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression
07:58

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Published on: February 24, 2023

Area of Science:

  • Mental Health Research
  • Computational Psychiatry
  • Global Public Health

Background:

  • Adolescent depression is a growing global concern, particularly in China, impacting development and social adaptation.
  • Traditional methods struggle with complex factors influencing adolescent mental health.
  • Machine learning (ML) offers advanced predictive capabilities for mental health disorders.

Purpose of the Study:

  • Compare ML algorithms for classifying depression risk in Chinese adolescents.
  • Identify key demographic, personality, and personal growth initiative (PGI) predictors.
  • Explore non-linear relationships and interactions among predictive factors.

Main Methods:

  • Trained and optimized seven ML algorithms using 5-fold cross-validation on data from 559 adolescents.
  • Analyzed feature importance using SHAP values and tested interaction effects via permutation tests.
  • Utilized Friedman and Nemenyi tests for model comparison and Youden's J statistic for threshold analysis.

Main Results:

  • LightGBM achieved the highest performance (AUC 0.834), accurately classifying depression risk.
  • Neuroticism was the strongest predictor, followed by proactive change, agreeableness, extraversion, and growth resilience.
  • Significant interactions were found between neuroticism and proactive change, and proactive change and agreeableness.

Conclusions:

  • ML, especially LightGBM, effectively identifies adolescent depression risk using personality and PGI factors.
  • Findings support integrating multi-dimensional variables for early intervention in adolescent mental health.
  • Reducing neuroticism and enhancing proactive growth behaviors may lower depression risk in Chinese adolescents.