ObjectiveTo analysis the affecting factors of Acupuncture Deqi by Data Mining. MethodsLiteratures about Acupuncture Deqi, which published from October 1949 to November 2013, were searched from Chinese-language databases (CNKI, WanFang, VIP and CBM) and PubMed database with main keywords "deqi" or "needle sensation" etc. The relational Modern Literatures Database about Acupucture Deqi database was established via Data Enging of Microsoft SQL Server 2005 Express Edition, and correlated documents were excavated via Apriori algorithm in Weka. ResultsThree hundred and thirty-seven studies were selected. Analyzed by Apriori algorithm, frequencies ranking of needle sensation among patients were swelling, numbness, conduction and soreness etc. from high to low and similarly hereinafter; and among health adults were pain, soreness, numbness and heaviness etc. Frequencies ranking of correlation analysis results among patients were heaviness-pain-numbness, soreness-pain-numbness, heaviness-soreness etc. and among health adults were swelling-soreness, heaviness-soreness-numbness, heaviness-soreness etc. ConclusionFunctional status of human body is an important affecting factor of Acupuncture Deqi.
To solve the problem of lacking of the subtypes of hypertension and the pathogenesis of complications in current clinical analysis, an analysis model involving integrating principal components analysis (PCA), K-means clustering algorithm, and Apriori algorithm was proposed in this article. Firstly, according to the redundant interference problem caused by the diversity of the patients' clinical index, the PCA theory was used to reduce the dimension and the redundant relationship. Secondly, on the basis of obtaining the main component of the clinical index data, the K-means algorithm was used to conduct the patients’ group analysis. Finally, the Apriori algorithm was used to analyze the frequent pattern of complications based on the complication data of different patients group. We used an example to verify efficacy of the above methods. The new analysis model of complications of hypertensive patients would provide an effective solution for the application of the current medical big data.