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Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

机译：基于机器学习的中医诊断与治疗的失眠研究

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Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.
机译：近年来，失眠作为中医（TCM）的主要疾病之一。为了探讨中医诊断和治疗研究新方法，本文提出了基于机器学习的失眠研究策略。首先，已从经验丰富的TCM医生作为样品数据收集654个失眠案件。其次，鉴于中医诊断和治疗的特点，研究样品的内容已分为四个部分：基本信息，四种诊断方法，基于综合征分化和主要处方。然后，通过三种分析方法分析了这四个部分，包括频率分析，关联规则和分级集群分析。最后，随机森林进行了整个四部分的全面研究。上述四个部分的研究揭示了一些重要的联系。同时，基于随机森林建立的算法模型，通过四种诊断方法的组合预测主要处方和基于综合征分化的治疗的准确性为0.85。此外，通过施加随机森林已经提取了特征，证明了五个Zang-organs的综合分化是中医诊断和治疗的最重要参数。结果表明，机器学习方法值得研究学习中医的主要疾病，探索诊断和治疗的关键规则。

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