The predictive and explanatory power of inductive decision trees: a comparison with artificial neural network learning as applied to the noninvasive diagnosis of coronary artery disease
Silver, D.L.; Hurwitz, G.A.
Journal of Investigative Medicine the Official Publication of the American Federation for Clinical Research 45(2): 99-108
1997
ISSN/ISBN: 1081-5589 PMID: 9084580 Document Number: 479447
Background: This paper compares two machine learning systems, an inductive decision tree (IDT) and a back-propagation neural network (ANN), in the noninvasive assessment of coronary artery disease given a set of diagnostic input attributes. A collection of 490 patient cases were accumulated from the reference of diagnostic stress myocardial scintigraphy performed in a nuclear medicine department. All cases had correlating angiography, the results of which were used to derive the target diagnoses. Input attributes included 4 baseline clinical characteristics, 4 nonimaging stress components, and 3 scintigraphic findings. Methods: We chose 4 possible angiographic criteria for coronary artery disease and assessed the ability of each learning system to develop a diagnostic model. The 2 machine learning systems were compared on the basis of predictive performance and explanatory power. Results; Cross-validation experiments showed the 2 machine learning systems to have equivalent predictive power at the same level as the clinical scan reading. For the 70% stenosis criterion, the IDT had a sensitivity of 94 +- 3% (mean +- 95% confidence interval) and a specificity of 59 +- 8%, and the ANN had a sensitivity of 97 +- 2% and a specificity of 51 +- 13%. However the IDT system exhibited excellent explanatory power; producing simple representations of the diagnostic models which agree with previous research. Conclusion: In comparison with the more widely used ANNs, the IDT learning system may bring advantages to certain problems in diagnostic classification.