On the Application of KNN Outlier Detection Algorithm in the Medical Insurance Verification

To solve the abuse and squandering of medical insurance fund, through the analysis of the data from a local medical insurance center, a simplified method, in which the major task of verification is to screen out those“suspicious” medi- cal prescriptions and let go of those normal ones, was found to reduce the workload of human verification. By use of the outlier detection algorithm and on the basis of solving the high dimensional sparsity after a pre-processing analysis of the medical in- surance data, a computational formula of attribute weights is worked out for medical insurance verification for the purpose of improving the detection accuracy; by applying the KNN algorithm to the detection of prescriptions of patients afflicted with cat- aract, gall-stone and appendicitis, it is found that the said algorithm is capable of screening out most“suspicious” medical pre- scriptions