Abstract
BACKGROUND: Loneliness is prevalent among the elderly and has intensified due to global aging trends. It adversely affects both mental and physical health. Traditional scales for measuring loneliness may yield biased results due to varying definitions. The advancements in machine learning offer new opportunities for improving the measurement and assessment of loneliness through the development of risk assessment models. METHODS: Data from the 2018 Chinese Longitudinal Healthy Longevity Survey, involving about 16,000 participants aged ≥ 65 years, were used. The study examined the relationships between loneliness and factors such as functional limitations, living conditions, environmental influences, age-related health issues, and health behaviors. Using R 4.4.1, seven assessment models were developed: logistic regression, ridge regression, support vector machines, K-nearest neighbors, decision trees, random forests, and multi-layer perceptron. Models were evaluated based on ROC curves, accuracy, precision, recall, F1 scores, and AUC. RESULTS: Loneliness prevalence among elderly Chinese was 23.4%. Analysis identified 15 evaluative factors and evaluated seven models. Multi-layer perceptron stands out for its strong nonlinear mapping capability and adaptability to complex data, making it one of the most effective models for assessing loneliness risk. CONCLUSION: The study found a 23.4% prevalence of loneliness among elderly individuals in China. SHAP values indicated that marital status has the strongest evaluative value across all forecasting periods. Specifically, elderly individuals who are never married, widowed, divorced, or separated are more likely to experience loneliness compared to their married counterparts.