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An inductive learning method for medical diagnosis

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An inductive learning method for medical diagnosis. / Lashkia, George V.; Anthony, Laurence.
In: Pattern Recognition Letters, Vol. 24, No. 1-3, 01.2003, p. 273-282.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Lashkia, GV & Anthony, L 2003, 'An inductive learning method for medical diagnosis', Pattern Recognition Letters, vol. 24, no. 1-3, pp. 273-282. https://doi.org/10.1016/S0167-8655(02)00241-6

APA

Vancouver

Lashkia GV, Anthony L. An inductive learning method for medical diagnosis. Pattern Recognition Letters. 2003 Jan;24(1-3):273-282. doi: 10.1016/S0167-8655(02)00241-6

Author

Lashkia, George V. ; Anthony, Laurence. / An inductive learning method for medical diagnosis. In: Pattern Recognition Letters. 2003 ; Vol. 24, No. 1-3. pp. 273-282.

Bibtex

@article{e4cf811d183e461594a794b95dc74325,
title = "An inductive learning method for medical diagnosis",
abstract = "In most learning models, the induction methods that are learnable have low expressive power. The learnability of such methods are proved for some concept classes by assuming that the hypothesis space of the method contains the target concept. However, in real-world practical problems the type of target concept being dealt with is almost always unknown. In medical diagnosis, where mistakes can cause fatal results, it is very important to achieve high recognition rates and the use of more expressive methods are common. However, conventional methods are weak at handling irrelevant information which often appears in medical databases. In this paper, we consider test feature classifiers recently introduced by (Lashkia and Aleshin, 2001. IEEE Trans. Syst. Man Cybern. 31 (4), 643–650), and show that they meet all essential requirements to be of practical use in medical decision making, which are: ability to handle irrelevant attributes, high expressive power, high recognition ability, and ability to generate decisions by a set of rules.",
keywords = "Medical diagnosis, Rule generation , Test feature classifier , Feature selection",
author = "Lashkia, {George V.} and Laurence Anthony",
year = "2003",
month = jan,
doi = "10.1016/S0167-8655(02)00241-6",
language = "English",
volume = "24",
pages = "273--282",
journal = "Pattern Recognition Letters",
publisher = "Elsevier Science B.V.",
number = "1-3",

}

RIS

TY - JOUR

T1 - An inductive learning method for medical diagnosis

AU - Lashkia, George V.

AU - Anthony, Laurence

PY - 2003/1

Y1 - 2003/1

N2 - In most learning models, the induction methods that are learnable have low expressive power. The learnability of such methods are proved for some concept classes by assuming that the hypothesis space of the method contains the target concept. However, in real-world practical problems the type of target concept being dealt with is almost always unknown. In medical diagnosis, where mistakes can cause fatal results, it is very important to achieve high recognition rates and the use of more expressive methods are common. However, conventional methods are weak at handling irrelevant information which often appears in medical databases. In this paper, we consider test feature classifiers recently introduced by (Lashkia and Aleshin, 2001. IEEE Trans. Syst. Man Cybern. 31 (4), 643–650), and show that they meet all essential requirements to be of practical use in medical decision making, which are: ability to handle irrelevant attributes, high expressive power, high recognition ability, and ability to generate decisions by a set of rules.

AB - In most learning models, the induction methods that are learnable have low expressive power. The learnability of such methods are proved for some concept classes by assuming that the hypothesis space of the method contains the target concept. However, in real-world practical problems the type of target concept being dealt with is almost always unknown. In medical diagnosis, where mistakes can cause fatal results, it is very important to achieve high recognition rates and the use of more expressive methods are common. However, conventional methods are weak at handling irrelevant information which often appears in medical databases. In this paper, we consider test feature classifiers recently introduced by (Lashkia and Aleshin, 2001. IEEE Trans. Syst. Man Cybern. 31 (4), 643–650), and show that they meet all essential requirements to be of practical use in medical decision making, which are: ability to handle irrelevant attributes, high expressive power, high recognition ability, and ability to generate decisions by a set of rules.

KW - Medical diagnosis

KW - Rule generation

KW - Test feature classifier

KW - Feature selection

U2 - 10.1016/S0167-8655(02)00241-6

DO - 10.1016/S0167-8655(02)00241-6

M3 - Journal article

VL - 24

SP - 273

EP - 282

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

IS - 1-3

ER -