Application of genetic algorithm and k-nearest neighbour method in medical fraud detection

Hongxing HE, Warwick GRACO, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

11 Citations (Scopus)

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). © Springer-Verlag Berlin Heidelberg 1999.
Original languageEnglish
Title of host publicationSimulated Evolution and Learning : Second Asia-Pacific Conference on Simulated Evolution and Learning, SEAL'98, Canberra, Australia, November 24-27, 1998 Selected Paper
EditorsBob MCKAY, Xin YAO, Charles S. NEWTON, Jong-Hwan KIM, Takeshi FURUHASHI
PublisherSpringer Berlin Heidelberg
Pages74-81
Number of pages8
ISBN (Electronic)9783540488736
ISBN (Print)9783540659075
DOIs
Publication statusPublished - 1999
Externally publishedYes
Event2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998 - Canberra, Australia
Duration: 24 Nov 199827 Nov 1998

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
Volume1585
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998
Country/TerritoryAustralia
CityCanberra
Period24/11/9827/11/98

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