A comparison of logistic regression to decision-tree induction in a medical domain.
This paper compares the performance of logistic regression to decision-tree induction in classifying patients as having acute cardiac ischemia. This comparison was performed using the database of 5773 patients originally used to develop the logistic-regression tool and test it prospectively. Both the ability to classify cases and the ability to estimate the probability of ischemia were compared on the default tree generated by the C4 version of ID3. They were also compared on a tree optimized on the learning set by increased pruning of overspecified branches, and on a tree incorporating clinical considerations. Both the LR tool and the improved trees performed at a level fairly close to that of the physicians, although the LR tool definitely performed better than the decision tree. There were a number of differences in the performance of the two methods, shedding light on their strengths and weaknesses.
Long
W J
WJ
MIT Laboratory for Computer Science, Cambridge, Massachusetts.
Griffith
J L
JL
Selker
H P
HP
D'Agostino
R B
RB
D000465:Algorithms; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data*; D005069:Evaluation Studies as Topic; D006801:Humans; D016015:Logistic Models; D017202:Myocardial Ischemia / Q000175:diagnosis; D012044:Regression Analysis