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.
Citation： JIANG Hongquan, WANG Gang, GAO Jianmin, JIANG Peng, GUO Qi. Analysis model of complications for hypertensive patients. Chinese Journal of Evidence-Based Medicine, 2017, 17(9): 1100-1105. doi: 10.7507/1672-2531.201705083 Copy