A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients.

Abstract

Logistic regression, classification tree, and neural network models all can provide excellent predictive performance of medical outcomes for clinical decision aids and policy models. Their ultimate limitations seem due to the availability of the information in data (a "data barrier") rather than their respective intrinsic properties. Choices between these methods would seem to be most appropriately based on the needs of the specific application, rather than on the premise that any one of these methods is intrinsically more powerful.