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Explainable Deep-Learning-Based Depression Modeling of Elderly Community after COVID-19 Pandemic

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

The impact of the COVID-19 epidemic on the mental health of elderly individuals is causing considerable worry. We examined a deep neural network (DNN) model to predict the depression of the elderly population during the pandemic period based on social factors related to stress, health status, daily changes, and physical distancing. This study used vast data from the 2020 Community Health Survey of the Republic of Korea, which included 97,230 people over the age of 60. After cleansing the data, the DNN model was trained using 36,258 participants’ data and 22 variables. We also integrated the DNN model with a LIME-based explainable model to achieve model prediction explainability. According to the research, the model could reach a prediction accuracy of 89.92%. Furthermore, the F1-score (0.92), precision (93.55%), and recall (97.32%) findings showed the effectiveness of the proposed approach. The COVID-19 pandemic considerably impacts the likelihood of depression in later life in the elderly community. This explainable DNN model can help identify patients to start treatment on them early.

Suggested Citation

  • Hung Viet Nguyen & Haewon Byeon, 2022. "Explainable Deep-Learning-Based Depression Modeling of Elderly Community after COVID-19 Pandemic," Mathematics, MDPI, vol. 10(23), pages 1-10, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4408-:d:981006
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    References listed on IDEAS

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    1. Kunho Lee & Goo-Churl Jeong & JongEun Yim, 2020. "Consideration of the Psychological and Mental Health of the Elderly during COVID-19: A Theoretical Review," IJERPH, MDPI, vol. 17(21), pages 1-11, November.
    2. Haewon Byeon, 2021. "Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    3. Nida Aslam & Irfan Ullah Khan & Samiha Mirza & Alanoud AlOwayed & Fatima M. Anis & Reef M. Aljuaid & Reham Baageel, 2022. "Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    4. Selçuk Özdin & Şükriye Bayrak Özdin, 2020. "Levels and predictors of anxiety, depression and health anxiety during COVID-19 pandemic in Turkish society: The importance of gender," International Journal of Social Psychiatry, , vol. 66(5), pages 504-511, August.
    5. Eun Young Choi & Mateo P Farina & Qiao Wu & Jennifer Ailshire, 2022. "COVID-19 Social Distancing Measures and Loneliness Among Older Adults," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 77(7), pages 167-178.
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