Construction and optimization of health behavior prediction model for the older adult in smart older adult care.

INTRODUCTION

With the intensification of global aging, health management for the older adult has become a significant societal concern. Addressing challenges such as data diversity, health status complexity, long-term dependence, and data privacy is crucial for predicting older adult health behaviors.

METHODS

This study designs and implements a smart older adult care service model incorporating modules like multimodal data fusion, data loss processing, nonlinear prediction, emergency detection, and privacy protection. It leverages multi-source datasets and market research for accurate health behavior prediction and dynamic management.

RESULTS

The model demonstrates excellent performance in health behavior prediction, emergency detection, and delivering personalized services. Experimental results show an increase in accuracy and robustness in health behavior prediction.

DISCUSSION

The model effectively addresses the needs of smart older adult care, offering a promising solution to enhance prediction accuracy and system robustness. Future improvements, integrating more data and optimizing technology, will strengthen its potential for providing comprehensive support in older adult care services.