Abstract
This paper presents a novel approach to the construction of reliable diagnostic rules from the available cases with known diagnoses. It proposes a simple and general framework based on the generation of the so-called confirmation rules. A property of a system of confirmation rules is that it allows for indecisive answers, which, as a consequence, enables that all decisive answers proposed by the system are reliable. Moreover, the consensus of two or more confirmation rules additionally increases the reliability of diagnostic answers. Experimental results in the problem of coronary artery disease diagnosis illustrate the approach.
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© 1999 Springer-Verlag Berlin Heidelberg
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Gamberger, D., Lavrač, N., Grošelj, C. (1999). Diagnostic Rules of Increased Reliability for Critical Medical Applications. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_39
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DOI: https://doi.org/10.1007/3-540-48720-4_39
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