Application of neural networks to detection of medical fraud

摘要

A multi-layer perceptron (MLP) network was trained to classify the practice profiles of a sample of medical general practitioners who had been classified by expert consultants into four classes ranging from having normal to having abnormal profiles. This method follows the two-class neural network classification of medical practice profiles developed at the Health Insurance Commission in 1990. A technique based on the probabilistic interpretation of the output of the neural network was used to see if it improved the performance of the MLP given the extent of noise (i.e. inconsistencies) in the experts' classifications. Kohonen's Self-Organising Map was also applied to analyse the consultants' classifications and it was found that an approach which combined the four classes into two was a more appropriate way to represent the classification data. The MLP network was then retrained using a two-class classification and a high agreement rate between the classifications of the MLP and the classifications of consultants was achieved.