Human readable rule induction in medical data mining

Nor Ridzuan Daud, David Wolfe Corne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

In general, the human-readable rule refers to data shown in a format easily read by most humans - normally this is in the form of IF...THEN rules. This is the most convenient way for physicians to express their knowledge in medical diagnosis. In particular, if learned diagnostic rules can be presented in such a form, physicians are much more likely to trust and believe the consequent diagnoses. This paper investigates the performances of existing state-of-the-art classification algorithms, mainly rule induction and tree algorithms, on benchmark problems in medical data mining. The findings indicate that certain algorithms are better for generating rules that are both accurate and short; these algorithms are recommended for further research towards the goal of improved accuracy and readability in medical data mining. © 2009 Springer Science+Business Media, LLC.

Original languageEnglish
Title of host publicationProceedings of the European Computing Conference
Pages787-798
Number of pages12
Volume27 LNEE
EditionVOL.1
DOIs
Publication statusPublished - 2009
EventEuropean Computing Conference - Athens, Greece
Duration: 25 Sep 200727 Sep 2007

Publication series

NameLecture Notes in Electrical Engineering
NumberVOL.1
Volume27 LNEE
ISSN (Print)1876-1100

Conference

ConferenceEuropean Computing Conference
CountryGreece
CityAthens
Period25/09/0727/09/07

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  • Cite this

    Daud, N. R., & Corne, D. W. (2009). Human readable rule induction in medical data mining. In Proceedings of the European Computing Conference (VOL.1 ed., Vol. 27 LNEE, pp. 787-798). (Lecture Notes in Electrical Engineering; Vol. 27 LNEE, No. VOL.1). https://doi.org/10.1007/978-0-387-84814-3_79