Predictions of coronary artery stenosis by artificial neural network.
Data from angiography patient records comprised 14 input variables of a neural network. Outcomes (coronary artery stenosis or none) formed both supervisory and output variables. The network was trained by backpropagation on 332 records, optimized on 331 subsequent records, and tested on final 100 records. If 0.40 was chosen as the output distinguishing stenosis from no stenosis, 81 patients who had stenosis would have been identified, while 9 of 19 patients who did not have stenosis might have been spared angiography. The results demonstrated that artificial neural networks could identify some patients who do not need coronary angiography.
Mobley
B A
BA
Department of Physiology, College of Medicine, University of Oklahoma, Oklahoma City, OK 73190, USA. bert-mobley@ouhsc.edu
Schechter
E
E
Moore
W E
WE
McKee
P A
PA
Eichner
J E
JE
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D017023:Coronary Angiography; D003327:Coronary Disease / Q000175:diagnosis*; D003657:Decision Making; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D018579:Patient Selection