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Predicting Depression during the COVID-19 Pandemic Using Interpretable TabNet: A Case Study in South Korea

Author

Listed:
  • Hung Viet Nguyen

    (Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae 50834, Republic of Korea)

  • Haewon Byeon

    (Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae 50834, Republic of Korea)

Abstract

COVID-19 has further aggravated problems by compelling people to stay indoors and limit social interactions, leading to a worsening of the depression situation. This study aimed to construct a TabNet model combined with SHapley Additive exPlanations (SHAP) to predict depression in South Korean society during the COVID-19 pandemic. We used a tabular dataset extracted from the Seoul Welfare Survey with a total of 3027 samples. The TabNet model was trained on this dataset, and its performance was compared to that of several other machine learning models, including Random Forest, eXtreme Gradient Boosting, Light Gradient Boosting, and CatBoost. According to the results, the TabNet model achieved an Area under the receiver operating characteristic curve value (AUC) of 0.9957 on the training set and an AUC of 0.9937 on the test set. Additionally, the study investigated the TabNet model’s local interpretability using SHapley Additive exPlanations (SHAP) to provide post hoc global and local explanations for the proposed model. By combining the TabNet model with SHAP, our proposed model might offer a valuable tool for professionals in social fields, and psychologists without expert knowledge in the field of data analysis can easily comprehend the decision-making process of this AI model.

Suggested Citation

  • Hung Viet Nguyen & Haewon Byeon, 2023. "Predicting Depression during the COVID-19 Pandemic Using Interpretable TabNet: A Case Study in South Korea," Mathematics, MDPI, vol. 11(14), pages 1-21, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3145-:d:1195658
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    References listed on IDEAS

    as
    1. Joanna F Dipnall & Julie A Pasco & Michael Berk & Lana J Williams & Seetal Dodd & Felice N Jacka & Denny Meyer, 2016. "Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-23, February.
    2. Hung Viet Nguyen & Haewon Byeon, 2023. "Prediction of Out-of-Hospital Cardiac Arrest Survival Outcomes Using a Hybrid Agnostic Explanation TabNet Model," Mathematics, MDPI, vol. 11(9), pages 1-17, April.
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