| Summary: | Traditional Chinese medicine (TCM) is effective in preventing and treating all manner of diseases, which has been incorporated into the latest global medical outline (Ver.2019) by World Health Organization (WHO). As one of the most important characteristics of TCM, syndrome differentiation (SD) provides curative effect assurance. SD is a high-dimensional complex function with symptoms/signs as input and syndrome type as output. Artificial neural network (ANN) provides an all-purpose data-driven solution to fit high-dimensional complex function, making ANN a promising approach for modeling intelligent SD (ISD) for TCM. In this paper, we chose chronic obstructive pulmonary disease (COPD) as an example for investigating ISD for TCM based on ANN. First, we built a full-group ANN model that combines ANN with full-group datasets composed of 18471 real clinical records. In addition, we built four extra models with ANN and four subgroup datasets. For comparison, we built another four models with four traditional machine-learning algorithms and the full-group datasets. We used accuracy and F1-scores to evaluate the models' performance. With an accuracy of 86.45% and an F1 score of 82.93%, the full-group ANN model outperformed the four comparison models built from traditional machine-learning algorithms, and however, the four subgroup models achieved a better performance than the full-group ANN model. We concluded that the ANN can potentially provide a way for ISD for TCM, and our subgroup modeling suggests ideas for further optimizing the ISD.
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