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An Interpretable Artificial Intelligence Model of Chinese Medicine Treatment Based on XGBoost Algorithm

机译：基于XGBoost算法的中医治疗可解释的人工智能模型

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摘要

TCM treatment model is an effective tool to provide correct guidance and decision-making in clinical practice. Recently, traditional Chinese medicine (TCM) has become an increasingly concerned problem in the world, and it is still a hot research topic. However, most machine learning researches pursue the performance of the model, but ignore the trust mechanism of decision-making process. The interpretable TCM treatment model based on XGBoost integration is constructed in this paper, and the interpretability of the model is taken into account when the performance is good. AUC is selected as the main evaluation index of model performance, and other commonly used evaluation indexes are added in the comparative experiment: accuracy. The results show that the average performance of the proposed model is better than that of the traditional logistic regression algorithm. The interpretability of the model is considered in the selection of base classifier, feature selection and model integration. Finally, the whole model and the decision explanation to the specific samples are provided.
机译：TCM治疗模式是在临床实践中提供正确指导和决策的有效工具。最近，中医（TCM）已成为世界上越来越有关的问题，仍然是一个热门的研究主题。然而，大多数机器学习研究追求模型的性能，但忽略了决策过程的信任机制。本文构建了基于XGBoost集成的可解释的TCM处理模型，当表现良好时，将考虑模型的可解释性。 AUC被选为模型性能的主要评估指标，并且在比较实验中添加了其他常用的评估指标：精度。结果表明，所提出的模型的平均性能优于传统的逻辑回归算法。在选择基本分类器，特征选择和模型集成的选择中考虑了模型的解释性。最后，提供了整个模型和对特定样品的决定说明。

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