Volume 20 Issue 2
Feb.  2022
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TONG Xu, YANG Chun, MENG Qing-gang. Risk assessment model of diabetic nephropathy with 'same disease and different syndromes' in traditional Chinese medicine based on multi-label machine learning[J]. Chinese Journal of General Practice, 2022, 20(2): 181-185, 227. doi: 10.16766/j.cnki.issn.1674-4152.002307
Citation: TONG Xu, YANG Chun, MENG Qing-gang. Risk assessment model of diabetic nephropathy with "same disease and different syndromes" in traditional Chinese medicine based on multi-label machine learning[J]. Chinese Journal of General Practice, 2022, 20(2): 181-185, 227. doi: 10.16766/j.cnki.issn.1674-4152.002307

Risk assessment model of diabetic nephropathy with "same disease and different syndromes" in traditional Chinese medicine based on multi-label machine learning

doi: 10.16766/j.cnki.issn.1674-4152.002307
Funds:

 81473800

 YZ-202118

  • Received Date: 2021-08-24
    Available Online: 2022-03-04
  •   Objective  To construct a risk assessment model of diabetic nephropathy with "same disease and different syndromes" in traditional Chinese medicine based on the multi-label machine learning algorithm and compare its effectiveness, and to provides an efficient way to assist traditional Chinese medicine in preventing and treating diabetic nephropathy.  Methods  Based on the data of 8 795 diabetic nephropathy, feature selection was carried out based on the complex network community detection algorithm. Under the two algorithms of "transformation problem" and "algorithm adaptation", the SVM, AdaBoost, ML-RBF and ML-KNN algorithms were used to construct the multi-label learning model, and five evaluation indexes were used to compare the model efficiency.  Results  A multi-label dataset of diabetic nephropathy with 8 795 samples, 113 characteristics and 15 syndrome types was constructed. In terms of model evaluation, ML-KNN had the best performance in Hamming loss, ranking loss and coverage indicators; SVM had three minimum values on one error index, but the average value of one error index of KNN was still the best. The average precision of the four models was more than 90%, and the performance of ML-KNN and ML-RBF were relatively the best. The above four models had better diagnostic efficiency in the multiple syndrome risk assessment of diabetic nephropathy with "same disease and different syndromes", and ML-KNN performance was relatively optimal.  Conclusion  The multi-label machine learning algorithm can be applied to the risk assessment of complex syndromes, such as TCM. It provides a reference for assisting Chinese medicine in the prevention and treatment of diabetic nephropathy and provides a methodological reference for the application of multi-label machine learning in clinical multi-disease diagnosis and treatment in general practice.

     

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