Abnormal Information Detection In Medical Consumption Of Social Security

Social health insurance has been assumed the protection of basic medical duties. With the continual investment of social health insurance, the management system of social health insurance is developed fast. The information management system of social security provides convenience for the management of social security. The number of insured people is increased, and different parts of the social security system have been accumulated a large amount of historical data. With the rapidly development of data mining technology, the theories are became matured, the data processing functions have been accepted and applied to data analysis. The introduction of data mining techniques in the social security management, excessive sedimentation of historical data for processing, to find out some hidden information and rules in large data sets, also, to prevent the risk of social security fund and control the security operations.In this thesis, the author first summarizes the problems sometimes encountered in the management of the social security medical insurance. Then, the theory of data mining methods, and cluster analysis is discussed. Later, introducing EM algorithm process, using the improved EM algorithm to select the initial parameters and maximize step simplifies the EM algorithm. SQL Server2008data mining platform on the basis of improved EM algorithm and plug-ins registered to the analysis server algorithm library, by comparing with the EM algorithm, improved the validity of the EM algorithm. Subsequently, the data warehouse for medical expenses anomaly detection topic in SQL Server2008to be created, by using a modified EM algorithm to create data mining models, this model, predict function to obtain the predictive value of various medical and consumer costs and data analysis through a series of rules, drawing the exception record of the costs of medical consumption. At the same time, the true abnormal consumption records are improved the EM algorithm and found out the exception record precision, the validity of the algorithm in practical applications is confirmed. The contents of this article obtained an analytical method to obtain the medical costs of abnormal consumption record.The main research of this thesis are as follows, firstly improved the EM algorithm, and validation to improve the effectiveness of the EM algorithm, secondly, created the data warehouse and generated the cube in the themes of detect the abnormal information in medical consumption of social security, and completed data mining on this basis analysis process, at last we have got the conclude that the result was useful. It’s an effective method of detect medical consumption abnormal information.