Knowledge discovery approach to automated cardiac SPECT diagnosis.
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.
Kurgan
L A
LA
University of Colorado at Denver, P.O. Box 173364, Denver, CO 80217-3354, USA. kcios@carbon.cudenver.edu
Cios
K J
KJ
Tadeusiewicz
R
R
Ogiela
M
M
Goodenday
L S
LS
D001185:Artificial Intelligence; D016208:Databases, Factual; D003658:Decision Making, Computer-Assisted; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis*; D015425:Myocardial Reperfusion; D015899:Tomography, Emission-Computed, Single-Photon