Predictions of coronary artery stenosis by artificial neural network

• Published in: Artificial intelligence in medicine Vol. 18; no. 3; pp. 187 - 203
• Main Authors: Mobley, Bert A., Schechter, Eliot, Moore, William E., McKee, Patrick A., Eichner, June E.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2000
• Subjects: Adult; Aged; Aged, 80 and over; Artificial neural networks; Coronary Angiography; Coronary artery disease; Coronary artery stenosis; Coronary Disease - diagnosis; Data evaluation; Decision Making; Female; Humans; Male; Middle Aged; Neural Networks (Computer); Outcome predictions; Patient Selection; Predictions; Coronary angiography; Outcome predictions; Coronary artery disease; Coronary artery stenosis; Artificial neural networks
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/S0933-3657(99)00040-8

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.