Abstract
Objective: To construct a common traditional Chinese medicine composite syndrome model based on multiple information processing methods. Methods: 1132 cases of colorectal cancer were collected by epidemiological investigation, and the case information of colorectal cancer patients was modeled by cluster analysis, BP neural network, SVM support vector machine and random forest method. Results: Among the syndrome models constructed by BP neural network, support vector machine and random forest, random forest had the best effect, and the recognition rate of each syndrome type was respectively: spleen deficiency and qi stagnation (65.1%), spleen and kidney yang deficiency (83.3%), kidney essence deficiency (92.3%), accumulation of damp and heat (97.7%), and deficiency of both qi and blood (96.3%). Conclusion: The common TCM complex syndrome model was successfully constructed, and the random forest method has the highest accuracy in judging syndrome types. The application of random forest modeling method can provide new ideas and methods for the standardization of TCM syndrome research.
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Lin, G., Huang, H., Chen, J., Xu, S., Yang, C. (2023). Construction of TCM Syndrome Model Based on Multiple Information Processing Methods. In: Hung, J.C., Yen, N.Y., Chang, JW. (eds) Frontier Computing. FC 2022. Lecture Notes in Electrical Engineering, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-99-1428-9_64
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DOI: https://doi.org/10.1007/978-981-99-1428-9_64
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