基于K-means聚类算法的住院费用数据挖掘

Data Mining of Hospitalization Expenses Based on K-means Clustering Algorithm

目前,我国医疗费用快速增长,人均门诊和住院费用的增长幅度远远大于人均收入增长幅度,医疗保险费用支出也大幅度提升。如何实现医疗保险费用控制是我国社会保险行业研究的一大热点问题。运用K-means聚类算法,对医保信息系统中记录的住院病人等信息进行挖掘,研究分析之间存在的内在联系,为合理控制医疗费用的过快增长提供参考。

At present, the hospitalization expenses in China is increasing rapidly, and the increasing range of per capita outpatient and hospitalization expenses is far greater than the increasing range of per capita income, thus medical insurance expenses is increasing significantly as well.How to realize the medical insurance expense control is a hotspot in the research of social insurance industry in our country. Adopts Kmeans clustering algorithm to mine information of hospitalized patients recorded in medical insurance system, so as to study and analyze the inner relations and provide reference for rational control of the excessive rapid growth of hospitalization expenses.