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Explainable artificial intelligence approaches for predicting depression by combining feature selection methods and

Min Gyeong Kim1, Kun Chang Lee2, Kwanho Lee2

  • 1SKKU Business School, Sungkyunkwan University, Seoul, Republic of Korea.

Digital Health
|January 23, 2026
PubMed
Summary
This summary is machine-generated.

This study combined feature selection with explainable AI (XAI) to improve depression prediction models. Key non-diagnostic factors like social distress and reluctance to seek help were identified as significant predictors.

Keywords:
DepressionNational Mental Health Survey of KoreaSHapley Additive exPlanations (SHAP)explainable artificial intelligence (XAI)feature selectionmachine learningmental healthpredictive models

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Mental Health Research
  • Public Health

Background:

  • Depression is a major global health issue with complex diagnosis and treatment.
  • Large-scale data analysis is crucial for understanding depression's multifaceted nature.
  • Explainable AI (XAI) offers potential for enhancing the interpretability of predictive models.

Purpose of the Study:

  • To enhance depression classification model accuracy using feature selection (FS) and explainable artificial intelligence (XAI).
  • To identify non-diagnostic socioeconomic, psychological, and lifestyle factors associated with depression.
  • To evaluate the impact of different FS-machine learning classifier combinations on model performance.

Main Methods:

  • Utilized microdata from the National Mental Health Survey of Korea (2021) with 5511 participants.
  • Employed diverse FS methods (ReliefF, Markov Blanket, Information Gain) across 12 machine learning classifiers.
  • Integrated SHapley Additive exPlanations (SHAP) for a dual-layer XAI framework.

Main Results:

  • Optimal FS method selection is dependent on the machine learning classifier architecture.
  • ReliefF excelled with Stacking (F2-score=0.9851), while Markov Blanket performed best with ExtraTrees and LightGBM.
  • Social distress, reluctance to seek help, quality of life, and physical comorbidities emerged as key non-diagnostic predictors.

Conclusions:

  • FS method effectiveness varies significantly across different machine learning classifiers.
  • A combined FS and SHAP framework provides comprehensive interpretability for depression prediction models.
  • Identified culturally specific risk factors in a Korean population, offering clinical insights for at-risk individuals.