Abstract
With the successful application of artificial intelligence technology in various fields, using intelligent identification to identify syndrome has attracted more attentions. On the one hand, there is a strong correlation among syndromes. On the other hand, the correlation among labels in Multi-Label Learning is one of the key factors affecting the performance of the algorithm. Firstly, relying on the key laboratory of TCM Data Cloud Service in the Universities of Shandong, we have obtained a clinical data set containing 2000 pieces of data. From it, we extract 62 columns for symptoms, 15 columns for observation, and 47 columns for inquiries. Then, we adopt Binary Relevance and ML-KNN multi-label model for training on the clinical dataset. We chase the best prediction performance of ML-KNN by adjusting the values of parameter K and the smoothing parameter S. Both theoretical analysis and experimental results prove that the recognition accuracy of the intelligent discrimination algorithm of TCM syndromes proposed in this paper can reach 81.27% on the real dataset.
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Acknowledgements
This work was supported by Shandong Management University Scientific Research Sailing Plan Project(QH2022Z01).
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Wang, C., Wang, N. (2023). Design and Implement Intelligent Discrimination of TCM Syndromes Based on Multi-label. In: You, P., Li, H., Chen, Z. (eds) Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). ICIVIS 2022. Lecture Notes in Electrical Engineering, vol 1019. Springer, Singapore. https://doi.org/10.1007/978-981-99-0923-0_102
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DOI: https://doi.org/10.1007/978-981-99-0923-0_102
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