Research on intelligent identification of Traditional Chinese Medicine constitutions based on multi-model fusion.
Year: 2026
Type: article
Abstract: OBJECTIVE: To develop an automated system for identifying and classifying constitution types in Traditional Chinese Medicine (TCM) by leveraging multi-model fusion algorithms. METHODS: A condensed version of a physical information collection form was designed to facilitate efficient data acquisition. The collected data were analyzed using a multi-model fusion approach, which integrated several machine learning techniques. These included support vector machines, Naive Bayes, decision trees, random forests, logistic regression, multilayer perceptrons, K-nearest neighbors, gradient boosting, adaptive ensemble learning, and recurrent neural networks. A soft voting strategy was used to combine the predictive outputs of each model, enabling the selection of the most effective model combination. RESULTS: deficiency constitution was relatively lower, indicating the need for further refinement and optimization in future research. CONCLUSION: This study introduces a novel, automated method for classifying TCM constitution types through the application of multi-model fusion algorithms. The approach simplifies the complex task of constitution identification while offering a practical and theoretical framework for the intelligent diagnosis of TCM body types. The findings have the potential to enhance personalized health management and support clinical decision-making in TCM diagnosis and treatment.
Source: PubMed
Authors: Yue Yao, Cai Xiaohong
Institution: Hubei University of Chinese Medicine
Language: English
Subfield: Complementary and alternative medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7128712590
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