高尿酸血症危险因素的数据挖掘及关联规则研究

目的使用关联规则的数据挖掘方法研究分析高尿酸血症危险因素的关联性及关联强度。 方法将调查的3 724例患者和社区居民的临床情况作为数据集，使用IBM SPSS Statistic19.0软件中的Apriori算法对数据集进行分析。 结果在3 724名调查人群中，高尿酸血症占样本的13.32%，三酰甘油、肌酐、尿素氮和总胆红素异常分别占25.76%、7.90%、13.91%和13.56%，高尿酸血症患病率分别为20.24%、43.70%、22.28%和20.72%。关联规则分析结果显示，高血压（χ²=34.01，P＜0.001）、糖尿病（χ²=9.07，P=0.003）、三酰甘油（χ²=20.38，P＜0.001）、总胆红素（χ²=57.03，P＜0.001）、谷丙转氨酶（χ²=6.156，P=0.017）、肌酐（χ²=102.71，P＜0.001）和尿素氮（χ²=72.82，P＜0.001）有统计学显著性差异；而血糖（χ²=0.369，P=0.584）、总胆固醇（χ²=1.081，P=0.326）和谷草转氨酶转氨酶（χ²=3.656，P=0.074）无统计学差异。 结论高血压、肌酐、三酰甘油和总胆红素是高尿酸血症的危险因素，高尿酸血症与高血压密切相关，是高血压的独立危险因素和预测因素。

Data mining and association rule mining of risk factors for hyperuricemia

ObjectiveTo analyze the association and correlation strength of risk factors for hyperuricemia using data mining and association rule mining. MethodsThe clinical data of 3724 patients and community residents were taken as the data set. The Apriori algorithm of IBM SPSS Statistic19.0 was used to analyze the data set. ResultsApproximately 13.32% of the patients had hyperuricemia. Abnormal triglycerides, creatinine, urea nitrogen, and total bilirubin occurred in 25.76%, 7.90%, 13.91%, and 13.56% of the patients, respectively. The prevalence of hyperuricemia in patients with abnormal triglycerides, creatinine, urea nitrogen, and total bilirubin was 20.24%, 43.70%, 22.28%, and 20.72%, respectively. Association rule analysis showed that hypertension (χ²=34.009, P＜0.001), diabetes (χ²=9.075, P=0.003), triglycerides (χ²=20.388, P＜0.001), total bilirubin (χ²=57.032, P＜0.001), ALT (χ²=6.156, P=0.017), creatinine (χ²=102.707, P＜0.001), and urea nitrogen (χ²=72.818, P＜0.001) were significantly different, although there were no significant differences in blood glucose (χ²=0.369, P=0.584), total cholesterol (χ²=1.081, P=0.326), and AST (χ²=3.656, P=0.074). ConclusionHypertension, Cr, TG, BUN, and total bilirubin are risk factors for hyperuricemia. Hyperuricemia is closely related to hypertension and is an independent risk factor and predictive factor for hypertension.