COMPARATIVE EXPERIMENT ON MODEL PREDICTION OF ELDERLY HOSPITALIZATION OR HOME STAY BASED ON HEALTH TEST DATA OF ELDERLY

By the end of 2023, China’s population aged 60 and above is projected to reach 296.97 million, or 21.1% of the total population, with those aged 65 and above numbering 216.76 million, accounting for 15.4%. This marks China’s transition into a moderately aged society. Addressing the diverse needs of the elderly and solving the social issues arising from population aging are crucial for the nation’s overall development and public welfare. A comprehensive analysis of the living environment of the elderly can aid in enhancing care services for this group. This study analyzes data from home-tested and hospitalized elderly patients to explore differences across various indicators. It classifies patients based on factors such as stress test comprehensive index, self-rating stress scale (SDS score), age, and gender, evaluating model performance using metrics like accuracy and recall. Principal component analysis (PCA) and K-means clustering have revealed key differences between the two groups, while Random Forest, Support Vector Machine (SVM), and XGBoost were employed for classification modeling to predict whether elderly patients are at home or in hospital. The results indicate significant differences in the feature space between the two patient groups, with feature analysis accurately predicting patient classification. This study offers valuable data support for elderly health management, suggesting that future research should consider more complex models or additional features to improve prediction accuracy and robustness. The findings highlight the importance of tailored care strategies to address the diverse needs of an aging population, ultimately contributing to better healthcare outcomes for the elderly.