Evaluating variable selection methods for diagnosis of myocardial infarction.
This paper evaluates the variable selection performed by several machine-learning techniques on a myocardial infarction data set. The focus of this work is to determine which of 43 input variables are considered relevant for prediction of myocardial infarction. The algorithms investigated were logistic regression (with stepwise, forward, and backward selection), backpropagation for multilayer perceptrons (input relevance determination), Bayesian neural networks (automatic relevance determination), and rough sets. An independent method (self-organizing maps) was then used to evaluate and visualize the different subsets of predictor variables. Results show good agreement on some predictors, but also variability among different methods; only one variable was selected by all models.
Dreiseitl
S
S
Harvard Medical School/Massachusetts Institute of Technology, Division of Health Sciences and Technology, Boston, USA. sdreisei@dsg.harvard.edu
Ohno-Machado
L
L
Vinterbo
S
S
D000465:Algorithms; D001185:Artificial Intelligence; D002637:Chest Pain / Q000209:etiology; D003936:Diagnosis, Computer-Assisted; D005069:Evaluation Studies as Topic; D006801:Humans; D016015:Logistic Models; D008433:Mathematics; D009203:Myocardial Infarction / Q000175:diagnosis*; D016571:Neural Networks, Computer