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Research on Syndrome Classification Prediction Model of Tibetan Medicine Diagnosis and Treatment Based on Data Mining

机译:基于数据挖掘的藏医学诊断与治疗综合征分类预测模型研究

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Tibetan Medicine plays an important role in traditional Chinese Medical Science. For the inheritance of Tibetan medical science and disease prevention, the main method is to summarize and study the medication and diagnostic rules by using data mining technology, which is still in the early stage. In this paper, firstly, a standard knowledge base for plateau stomach illness has been constructed by using clustering algorithm to analyze clinical diagnosis and treatment data and Tibetan medicine prescription that from "Four Medical Classics". Secondly, the classical Apriori algorithm is used to discover the associated features of disease symptoms and prescriptions. Finally, an ovel distance discriminant based K-nearest neighbor algorithm on the base of the Grey Box method is put forward to realize the Tibetan medicine diagnosis and treatment prediction model on the plateau stomach illness (Atrophic Gastritis) through combining the individual characteristics of patients and typical symptoms of plateau stomach. As a result, the model of this paper can achieve an accuracy as high as 80.1%.
机译:西藏医学在中国传统医学科学中起着重要作用。对于藏族医学和疾病预防的遗传,主要方法是通过使用数据挖掘技术来总结和研究药物和诊断规则,仍处于早期阶段。本文通过使用聚类算法通过聚类算法分析来自“四医学经典”的临床诊断和治疗数据和藏医药处方的高原胃病的标准知识库。其次,经典的Apriori算法用于发现疾病症状和处方的相关特征。最后,提出了在灰盒法的基础上基于灰距离判别的基于邻居算法,以实现高原胃病(萎缩性胃炎)的藏医学诊断和治疗预测模型通过组合患者的个性特征和高原胃的典型症状。结果,本文的模型可以达到高达80.1%的精度。

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