Knowledge discovery approach to automated cardiac SPECT diagnosis

Authors:
Lukasz A. Kurgan;Krzysztof J. Cios;Ryszard Tadeusiewicz;Marek Ogiela;Lucy S. Goodenday
Affiliations:
University of Colorado at Denver, P.O. Box 173364, Denver, CO 80217-3354, USA and University of Colorado at Boulder, Boulder, CO, USA;University of Colorado at Denver, P.O. Box 173364, Denver, CO 80217-3354, USA and University of Colorado at Boulder, Boulder, CO, USA and University of Colorado Health Science Center, Denver, CO, ...;University of Mining and Metallurgy, Krakow, Poland;University of Mining and Metallurgy, Krakow, Poland;Medical College of Ohio, Toledo, OH, USA
Venue:
Artificial Intelligence in Medicine
Year:
2001

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Abstract

The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.