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

Hung Viet Nguyen and Haewon Byeon ()
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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

Mathematics, 2022, vol. 10, issue 23, 1-10

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.

Keywords: deep learning; deep neural network; LIME; explainable AI; depression; post-COVID-19 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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