Author/Editor     Gamberger, Dragan
Title     Specific rule induction for medical domains
Type     članek
Source     In: Lavrač N, editor. CADAM-95. Zbornik Računalniška analiza medicinskih podatkov; 1995 nov 27-28; Bled. Ljubljana: Inštitut Jožef Štefan,
Publication year     1995
Volume     str. 136-45
Language     eng
Abstract     In the paper some rule induction methods specific for medical domains are presented. As an example the application of inductive learning system ILLM to a breast cancer domain is described. The learning domain has been 699 cases of fine-needle aspiration biopsy collected in the Wisconsin Breast Cancer Database. The unique characteristics of the ILLM have been used to construct the rules of increased sensitivity and improved interobserver reproducibility with a hope that these properties might significantly influence the diagnosis reliability in practical applications. In the work a complete description of the generated rules that might be used by physicians and computer based systems, if attribute coding is done in accordance with the learning cases, is offered.
Descriptors     BREAST NEOPLASMS
BIOPSY, NEEDLE
ARTIFICIAL INTELLIGENCE
DECISION MAKING