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: The classification models demonstrated consistent and robust performance across most TCM constitution types when enhanced by the multi-model fusion strategy. In particular, high levels of accuracy, precision, recall, and F1-score were achieved for constitution types such as Yang deficiency, Qi deficiency, and Qi stagnation. However, the classification performance for the Yin 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.