基于Apriori算法的个人医疗费用关联规则分析

目的：利用Apriori算法分析个人医疗费用的关联规则，为相关科研人员今后的研究方向提供参考。方法：对源数据预处理后，使用R软件进行关联规则分析，采用Apriori算法挖掘出与高额医疗费用有强关联的因素。结果：得出45条符合当前设置支持度和置信度的关联规则，调用R软件的绘图函数，将挖掘结果可视化。结论：高龄、吸烟与高额医疗费用有较为显著的关联，低龄人群中低医疗费用者与不吸烟关联显著，在高龄人群中女性高额医疗费用的特征比男性更明显，男性中的吸烟者有较高概率出现高额医疗费用，其他如BMI指数、居住地、医疗保险覆盖的儿童等指标与高额医疗费用无明显关系。

Apriori algorithm-based analysis of correlation rules for individual medical expenditure

Objective To provide reference for those engaged in study of individual medical expenditure by analyzing the correlation rules for individual medical expenditure with Apriori algorithm. Methods The correlation rules for individual medical expenditure were analyzed using R software after the source data were preprocessed. The factors related with high medical expenditure were mined using Apriori algorithm. Results Forty-five correlation rules for individual medical expenditure in line with the current set of support and confidence were mined and visualized by calling the plotting function from R software. Conclusion Advanced age and smoking are closely correlated with high medical expenditure, non-smoking young persons are closely correlated with low medical expenditure and high medical expenditure of very old females is not correlated with that of very old males.