Machine learning improves the accuracy of coronary artery disease diagnostic methods

The diagnostic process of coronary artery disease (CAD) consists of evaluation of symptoms and signs of the disease and ECG at rest, ECG during exercise, myocardial perfusion scintigraphy (MPS) and coronary angiography. Machine Learning (ML) can use all particular data in interpretation of result. T...

Full description

Saved in:
Bibliographic Details
Published inComputers in Cardiology 1997 pp. 57 - 60
Main Authors Groselj, C., Kukar, M., Fettich, J.J., Kononenko, I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Angiography
Arteries
Blood flow
Cardiac disease
Cardiovascular diseases
Coronary arteriosclerosis
Electrocardiography
Heart
Machine learning
Myocardium
Online AccessGet full text

Cover

More Information
Summary:The diagnostic process of coronary artery disease (CAD) consists of evaluation of symptoms and signs of the disease and ECG at rest, ECG during exercise, myocardial perfusion scintigraphy (MPS) and coronary angiography. Machine Learning (ML) can use all particular data in interpretation of result. The authors' goal was to predict in a group of 327 patients the results of coronary angiography obtained by ML method and compare them with the results of MPS as the highest step in the classical diagnostic procedure. The Naive Bayesian Classifier as one of the ML methods was applied. The sensitivity of MPS was 0.83 and specificity 0.85. The post-test probability for CAD was 0.75 for positive results and 0.43 for negative ones. With application of ML the authors achieved sensitivity 0.89, specificity 0.88 and the post-test probability 0.90 for positive and 0.25 for negative results.
ISBN:9780780344457
0780344456
ISSN:0276-6547
DOI:10.1109/CIC.1997.647829