An inductive learning method for medical diagnosis

George V. Lashkia*, Laurence Anthony

*この研究の対応する著者

研究成果: Article査読

6 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)273-282
ページ数10
ジャーナルPattern Recognition Letters
24
1-3
DOI
出版ステータスPublished - 2003 1
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

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