Multi-Information Fusion Method for Traditional Chinese Medicine Constitution Identification in the Elderly

Objective: This study addresses the limitations of existing traditional Chinese medicine (TCM) constitution identification techniques for the elderly by proposing an intelligent identification method aimed at enhancing the accuracy, standardization, and formalization of the identification process. Materials and Methods: Leveraging data from the images of the tongue, face, and pulse, this study introduced four image classification models: EfficientNetV2, MobileViT, Vision Transformer, and Swin Transformer. A comparative experimental approach was employed to establish a baseline model. Subsequently, a multi-information fusion model was constructed on this foundation, extracting integrated features from diverse data to further improve identification accuracy. Results: The multi-information fusion model developed in this study achieved an accuracy of 71.32%, effectively enhancing the accuracy of TCM constitution identification for the elderly. Conclusions: The multi-information fusion model developed in this study, by integrating tongue, facial, and pulse data, considerably enhances the accuracy of TCM constitution identification. It effectively addresses the certain limitations inherent in existing TCM constitution identification techniques, offering a novel and efficacious strategy for this domain.