The Application And Research Of Data Mining In Yili Region's Medical Insurance Monitoring

With the continuous development of Chinese medical insurance,medical insurance coverage gradually expanded year by year,improve the overall level,increasing the number of insured people,the number of visits to medical insurance designated medical institutions daily rapid growth.At the same time,the medical insurance fund balance scale more and more big,the medical insurance interests of the parties involved in the main body of their own,leading to the practice of medical insurance fraud chaos,such as cheating,excessive medical consumption etc.,including the use of false invoices,false documents to defraud Medicare Fund,repeated visits repeat medicine,medical care,health insurance documents impersonation medicine the use of insurance funds to pay medical expenses,medical insurance object or the range of medicines and medical services etc..In order to ensure the healthy development of the medical insurance in our district,the application of data mining technology in the medical insurance management and supervision has important practical significance.This paper takes the Yili Kazak Autonomous Prefecture of the medical insurance business as the research object,the medical insurance insured in 2016 890 thousand statewide data extraction,through metadata analysis,granularity and hierarchy,data extraction,data conversion and data loading steps such as the establishment of a data warehouse.This paper mainly uses the k-means algorithm to analyze the abnormal data of key diseases and the rationality of the cost structure.Through the sample data,found in the first half of this year's treatment,the most hypertensive patients,can provide leadership for decision-making advice,focus on medical treatment of hypertension disease resources,or to remind the people pay attention to exercise.According to the Yili Kazak Autonomous Prefecture medical insurance agency business requirements established,high cost,frequent medical treatment and repeated treatment,excessive unreasonable use of medicine,medical treatment,hospitalization and other monitoring false decomposition rules,aggregate query,drill down query,run by the polymerization of k-Means algorithm of massive medical information data mining technology to achieve a statistical analysis model for monitoring rules.Through data visualization technology to analyze the statistical results of the intuitive display.