Examination of Social Participation in Older Adults Undergoing Frailty Health Checkups Using Deep Learning Models.

Background/Objectives: Frailty in older adults limits social participation. We aimed to predict social participation in older individuals undergoing frailty health checkups using three machine learning (ML) models and identify key predictive factors through deep neural network (DNN) analysis. Methods: Overall, 301 older individuals were enrolled; 295 were included in the final analysis. The survey measured 18 attributes, including demographic, physical, cognitive, and social factors. Logistic regression (LR), nonlinear support vector machine (NLSVM), and DNN were used for prediction, with precision, accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) calculated as evaluation metrics. Results: Among 295 participants, 236 (80%) engaged in social activities, whereas 59 (20%) did not. The three models demonstrated complementary strengths: DNN provided the most balanced performance with superior sensitivity for detecting social participants; NLSVM showed the best overall discriminative ability but with higher false positive rates; and LR achieved the highest precision for correctly identifying participants but missed detecting social participants. AUC values ranged from 0.776 to 0.795 across models, indicating moderate discriminative performance. Contribution analysis revealed information-collection ability as the strongest predictor of social participation, followed by walking speed and number of cohabitants. Conclusions: ML models achieved moderate discriminative performance for predicting social participation among frailty-screened older adults. The DNN provided the most balanced performance. Each model exhibited distinct characteristics suitable for different screening purposes, with information-collection ability emerging as a key factor. The findings suggest that models must be carefully selected based on specific community health screening objectives.