Journal
ARTIFICIAL INTELLIGENCE IN MEDICINE
Volume 23, Issue 2, Pages 149-169Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/S0933-3657(01)00082-3
Keywords
knowledge discovery and data mining; SPECT myocardial perfusion imaging; CLIP3 machine learning algorithm
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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. (C) 2001 Elsevier Science B.V. All rights reserved.
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