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Application of Genetic Algorithm and k-Nearest Neighbour Method in Medical Fraud Detection

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Simulated Evolution and Learning (SEAL 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1585))

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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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Author information

Authors and Affiliations

  1. Health Insurance Commission, 134 Reed Street, P. O. Box 1001, Tuggeranong, ACT, 2900, Australia

    Hongxing He & Warwick Graco

  2. School of Computer Science, University College, University of New South Wales, ACT, 2600, Australia

    Xin Yao

Authors
  1. Hongxing He
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  2. Warwick Graco
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  3. Xin Yao
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Editor information

Editors and Affiliations

  1. School of Computer Science, University College, UNSW Australian Defence Force Academy, Canberra, ACT, Australia, 2600

    Bob McKay , Xin Yao  & Charles S. Newton ,  & 

  2. Department of Electrical Engineering Korea Advanced Institute of Science and Technology, 373-1, Kusung-dong, Yusung-gu, Taejon-shi, 305-701, Korea

    Jong-Hwan Kim

  3. Department of Information Electronics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

    Takeshi Furuhashi

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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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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65907-5

  • Online ISBN: 978-3-540-48873-6

  • eBook Packages: Springer Book Archive

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