Abstract
K-nearest neighbour (KNN) algorithm in combination with a genetic algorithm were applied to a medical fraud detection problem. The genetic algorithm was used to determine the optimal weighting of the features used to classify General Practitioners’ (GP) practice profiles. The weights were used in the KNN algorithm to identify the nearest neighbour practice profiles and then two rules (i.e. the majority rule and the Bayesian rule) were applied to determine the classifications of the practice profiles. The results indicate that this classification methodology achieved good generalisation in classifying GP practice profiles in a test dataset. This opens the way towards its application in the medical fraud detection at Health Insurance Commission (HIC).
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© 1999 Springer-Verlag Berlin Heidelberg
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He, H., Graco, W., Yao, X. (1999). Application of Genetic Algorithm and k-Nearest Neighbour Method in Medical Fraud Detection. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_11
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DOI: https://doi.org/10.1007/3-540-48873-1_11
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