ArticleScientific reports2023
Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Prevalence and related influencing factors of depression symptoms among empty-nest elderly in China.Scientific reports · 2026Article
- Machine Learning-Based Depression Risk Prediction Models for Older Adults Analyzing From the Perspective of the Health Ecology Model: A Scoping Review.Neuropsychiatric disease and treatment · 2026Review
- Psychometric evaluation of the Chinese version of the Geriatric Feelings of Burdensomeness Scale (GFBS) and analysis of associated factors.BMC geriatrics · 2025Article
- Predicting depression risk in middle-aged and elderly adults in China using CNN-BiLSTM-Attention mechanism and LSTM+SHAP framework.BMC psychiatry · 2025Article
- Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households.BMC health services research · 2025Article
- Machine learning approach for unmet medical needs among middle-aged adults in South Korea: a cross-sectional study.BMC health services research · 2025Article
- Factors Associated With Self-Rated Health Among Older Adults in Japan: A Decision Tree Analysis.Cureus · 2025Article
- Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.Scientific reports · 2025Article
- Analysis of influencing factors and equity in hospitalization expense reimbursement for mobile populations based on random forest model: a cross-sectional study from China.Frontiers in public health · 2025Article
- Analysis of influencing factors and equity in health education for mobile populations based on random forest model: evidence from the China migrants dynamic survey.Frontiers in public health · 2025Article
- Analysis of key factors and equity in influenza vaccination among Chinese adults-evidence from a large national cross-sectional survey.Frontiers in public health · 2025Article
- Association between precarious employment and the onset of depressive symptoms in men and women: a 13-year longitudinal analysis in Korea (2009-2022).Epidemiology and psychiatric sciences · 2024Article
- Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.Scientific reports · 2024Article
- Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation.Scientific reports · 2024Article
- The Association Between Internet Use and Mental Health Among Empty-Nest Elderly: A Cross-Sectional Study.Inquiry : a journal of medical care organization, provision and financingArticle
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4 authors.
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Abstract
Korea is showing the fastest trend in the world in population aging; there is a high interest in the elderly population nationwide. Among the common chronic diseases, the elderly tends to have a high incidence of depression. That said, it has been vital to focus on preventing depression in the elderly in advance. Hence, this study aims to select the factors related to depression in low-income seniors identified in previous studies and to develop a prediction model. In this study, 2975 elderly people from low-income families were extracted using the 13th-year data of the Korea Welfare Panel Study (2018). Decision trees, logistic regression, neural networks, and random forest were applied to develop a predictive model among the numerous data mining techniques. In addition, the wrapper's stepwise backward elimination, which finds the optimal model by removing the least relevant factors, was applied. The evaluation of the model was confirmed via accuracy. It was verified that the final prediction model, in the case of a decision tree, showed the highest predictive power with an accuracy of 97.3%. Second, psychological factors, leisure life satisfaction, social support, subjective health awareness, and family support ranked higher than demographic factors influencing depression. Based on the results, an approach focused on psychological support is much needed to manage depression in low-income seniors. As predicting depression in the elderly varies on numerous influencing factors, using a decision tree may be beneficial to establish a firm prediction model to identify vital factors causing depression in the elderly population.
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