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

Abstract

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 accu-mulated 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 speci-ficity of 51 ± 13%. However the IDT system exhibited excel-lent 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.