Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -