ArticleScientific reports2025
Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine Learning-Based Multidimensional Health Decline Prediction Framework: Data-Driven Modeling for the Middle-Aged and Elderly Population.Healthcare (Basel, Switzerland) · 2026Article
- Age-related determinants of geriatric depression: an analysis of theory-driven and data-driven approaches.BMC health services research · 2026Article
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- Late-Onset Depression in an Aging World: A Multidimensional Perspective on Risks, Mechanisms, and Treatment.Geriatrics (Basel, Switzerland) · 2026Review
- 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
- Article
- Third-degree atrioventricular block induced by escitalopram and quetiapine in a patient with depression: a case report.American journal of translational research · 2025Article
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4 authors.
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Abstract
Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey 'Korean Longitudinal Study of Aging,' we employed latent growth modeling and growth mixture modeling to identify four latent classes of depression trajectories in the elderly Korean population. Based on the results of binary logistic regression, we selected 12 variables capable of distinguishing the LOD population from the reference population and tested 12 machine learning (ML) algorithms. While most ML algorithms showed acceptable predictive capability, Random Forest Classifier and Gradient Boosting Classifier demonstrated superior performance. Consequently, we successfully established new ML-based LOD prediction programs. These programs could be further developed into self-checking online tools, expected to serve as decision support systems for primary medical care and health screening services.
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