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Bayesian networks and chained classifiers based on SVM for traditional chinese medical prescription generation

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Abstract

Traditional Chinese Medicine(TCM) is playing an increasingly prominent role in lung cancer treatment, as it can prolong patients’ survival, improve their quality of life, and reduce the adverse effects of radiotherapy and chemotherapy. However, the effectiveness of TCM treatment depends more on the personal experience of doctors, and the standardization of TCM prescriptions needs to be strengthened. In this study, we use TCM clinical prescriptions to train a standardized TCM prescription generation model to provide an auxiliary prescription reference for physicians. However, in our initial experiments, we found two severe problems in the dataset. The first problem is a strong correlation between each herb; for instance, some herbs often appear together to treat specific symptoms. The second is a severe class imbalance within each label, a few herbs always appear in most prescriptions, but most herbs have a low frequency of occurrence in the total dataset. To solve the correlation between each herb label, we adopt the Bayes Classifier Chain(BCC) algorithm, whose basic classifier is Cost-Sensitive SVM targeted to the class imbalance of the label. Based on this, we also improve the BCC method according to the characteristics of TCM prescription dataset. In our BCC classifier, the Directed Acyclic Graph (DAG) construction method has high interpretability in the scenario of TCM prescription. After combining multi-label learning algorithms with several SVM algorithms and comparing their performance in detail, we find that BBC+CS-SVM best deals with class imbalance within the label in multi-label classification problems.

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Acknowledgements

This work is supported by the National Science Foundation of China (No. 61672161).

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Authors and Affiliations

  1. School of Computer Scinece, Fudan University, Shanghai, China

    Yingpei Wu & Chaohan Pei

  2. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

    Chaohan Pei, Ruofei Wang & Yanchun Zhang

  3. School of Economics and Finance, Shanghai International Studies University, Shanghai, China

    Chunyang Ruan

  4. Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, China

    Yun Yang

  5. Oncology Institute of Traditional Chinese Medicine, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai, 200071, China

    Yun Yang

  6. Oujiang laboratory, (Zhejiang lab for Regenerate Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325001, China

    Yingpei Wu & Yanchun Zhang

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  1. Yingpei Wu
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  2. Chaohan Pei
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  3. Chunyang Ruan
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  4. Ruofei Wang
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  5. Yun Yang
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  6. Yanchun Zhang
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Correspondence to Chunyang Ruan.

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Wu, Y., Pei, C., Ruan, C. et al. Bayesian networks and chained classifiers based on SVM for traditional chinese medical prescription generation. World Wide Web 25, 1447–1468 (2022). https://doi.org/10.1007/s11280-021-00981-5

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  • DOI: https://doi.org/10.1007/s11280-021-00981-5

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