Service quality evaluation of integrated health and social care for older Chinese adults in residential settings based on factor analysis and machine learning

Objective To evaluate the service quality of integrated health and social care institutions for older adults in residential settings in China, addressing a critical gap in the theoretical and empirical understanding of service quality assurance in this rapidly expanding sector. Methods This study employs three machine learning algorithms—Backpropagation Neural Networks (BPNN), Feedforward Neural Networks (FNN), and Support Vector Machines (SVM)—to train and validate an evaluative item system. Comparative indices such as Mean Squared Error, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and predictive performance metrics were employed to assess the models. Results The service quality evaluation model, enhanced by factor analysis and fuzzy BPNN, demonstrated reduced error rates and improved predictive performance metrics. Key factors influencing service quality included daily care, medical attention, recreational activities, rehabilitative services, and psychological well-being, listed in order of their impact. Conclusion The BPNN-based model provides a comprehensive and unified framework for assessing service quality in integrated care settings. Given the pressing need to match service supply with the complex demands of older adults, refining the service delivery architecture is essential for enhancing overall service quality.