Factors associated with admission to elderly medical-welfare facilities in South Korea: a cross-sectional machine-learning study.

Abstract

To identify the key factors associated with admission to elderly medical-welfare facilities in South Korea and to evaluate their relative importance using machine learning techniques, providing an evidence base for policy in a rapidly ageing society. A cross-sectional secondary data analysis. The analysis was conducted using the National Health Insurance Service -Senior database, a large-scale public dataset constructed to be statistically representative of the elderly population in South Korea. A total of 48 614 elderly individuals aged 60-80 years, selected through a scientifically rigorous stratified random sampling method. The primary outcome was the binary classification of an individual's long-term care arrangement: admission to a facility-based service versus utilisation of home-based care. The secondary outcome was the relative importance and contribution of a wide range of demographic, health-status and care-related variables in predicting facility admission. After addressing severe class imbalance with the synthetic minority over-sampling technique, our final random forest model demonstrated excellent predictive power, achieving a sensitivity of 0.856 and a balanced accuracy of 0.921. An analysis of feature importance using Shapley Additive Explanations (SHAP) revealed that the most dominant predictor was an individual's cohabitation with institutional staff (mean SHAP value ≈ 0.180), indicating pre-existing contact with the formal care system. Other critical factors included the absence of a primary caregiver (≈ 0.056), being fully dependent due to dementia (≈ 0.017) and the degree of functional impairment as measured by the activities of daily living (ADL) score (≈ 0.017). Admission to medical-welfare facilities in South Korea is a multifactorial issue, most strongly driven by the erosion of informal care support systems combined with severe health decline, particularly in cognitive and physical function. These evidence-based findings highlight the need for policy interventions that strengthen community-based integrated care and enhance support for family caregivers. Such strategies are essential for promoting ageing-in-place, ensuring the long-term sustainability of the public long-term care system and ultimately improving the quality of life for the nation's growing elderly population.