This paper evaluates the variable selection performed by several machine-learning techniques on a myocardial infarction data set. The focus of this work is ...
Evaluating variable selection methods for diagnosis of myocardial ...
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Evaluating the variable selection performed by several machine-learning techniques on a myocardial infarction data set shows good agreement on some ...
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FULL TEXT Abstract: This paper evaluates the variable selection performed by several machine-learning techniques on a myocardial infarction data set.
Our results indicate that some of the examined methods are well suited for variable selection in logistic regression and that our model, and our myocardial ...
Nov 13, 2019 · Variable selection is an important issue when developing prognostic models. Missing data occur frequently in clinical research.
Evaluating Variable Selection Methods for Diagnosis of Myocardial Infarction. Dreiseitl, S., Ohno-Machado, L., & Vinterbo, S. JAMIA, Suppl. S:246–250, 1999.
Bibliographic details on Evaluating variable selection methods for diagnosis of myocardial infarction.
Here we evaluate the use of several methods for selection of variables in logistic regression. We use a large dataset to predict the diagnosis of myocardial ...
The objective of this study was to determine the reproducibility of logistic regression models developed using automated variable selection methods.
Feb 28, 2009 · Here we evaluate the use of several methods for selection of variables in logistic regression. We use a large dataset to predict the diagnosis ...