The Application And Research On Data Mining Of Aging Problem Among The Medical Insurance Officers

Life expectancy of aging population has become a worldwide trend. According to the United Nations'standards, all the developed countries have been in an aging society,and many developing countries is being or is about to enter an aging society.How to provide the effective medical security for the large number of the older group is a social problem need to further study.Whether the endowment insurance of our country has plenty of funds to cope with the future crisis of aging population or not, which has becoming the government and the public concern. By using the correlation analysis method of data mining, this paper studied the fund balance of social medical insurance, and deeply analyzed the impact that the insured people, insured units, medical units and other factors put on the stable operation of the social medical insurance funds, which can provide a powerful technical support for timely adjustment of fund collection policy for social security departments and the smooth implementation of the medical insurance system.This paper analyzed the origin, development status and the principle of data mining, studied the architecture and workflow of data mining. Selecting the correlation analysis as the main method, Correlation analysis is one of important technologies of data mining, which aims to discover the large amounts of data in the association between item sets and to generate the association rules. Therefore, data mining, especially association analysis can effectively analyzing the income and expenditure data of medical insurance fund, and mining the relationships between the insured person of different identity and ages and fund expenses of medical insurance.This paper preprocessed all the collected data, building a data warehouse with star model in the choice of dimensional modeling, and re-organized the structured relations of the source data, we use Apriority algorithm improved the data warehouse in the process of Data mining phase, which made three association rules of data mining that are the data relationship between the income of the insured person and health expenditure, the data relationship among common diseases, hospitalization data and age information of seeing doctors.As there is not yet a complete data warehouse and data mining technology to solve the fund's risk controlling applications in the domestic social health insurance system, this paper proposed a comprehensive set of implementation options from a practical point of view, which is considered to be advanced and practical to some extent.