A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction.
Actual use of regression models in clinical practice depends on model simplicity. Reducing the number of variables in a model contributes to this goal. The quality of a particular selection of variables for a logistic regression model can be defined in terms of the number of variables selected and the model's discriminatory performance, as measured by the area under the ROC curve. A genetic algorithm was applied to search for the best variable combinations for modeling presence of myocardial infarction in a data set of patients with chest pain. Using an external validation set, the resulting model was compared with models constructed with standard backward, forward and stepwise methods of variable selection. The improvement in discriminatory ability yielded by the genetic algorithm variable selection method was statistically significant (p < 0.02).
Vinterbo
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Decision Systems Group, Harvard Medical School/Massachusetts Institute of Technology, Boston, USA.
Ohno-Machado
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D000465:Algorithms; D002637:Chest Pain / Q000209:etiology; D005069:Evaluation Studies as Topic; D005823:Genetics; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D009203:Myocardial Infarction / Q000175:diagnosis*; D012372:ROC Curve