Aligning Bayesian Network Classifiers with Medical Contexts (2008)
BibTeX
@MISC{Gaag08aligningbayesian,
author = {Linda C. Van Der Gaag and Silja Renooij and Ad Feelders and Arend De Groote and Marinus J. C. Eijkemans and Frank J. Broekmans and Bart C. J. M. Fauser and Linda C. Van Der Gaag and Silja Renooij and Ad Feelders and Arend De Groote and Marinus J. C. Eijkemans and Frank J. Broekmans and Bart C. J. M. Fauser},
title = {Aligning Bayesian Network Classifiers with Medical Contexts},
year = {2008}
}
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Abstract
While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become as widely accepted within the medical community as logistic regression models. We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of comparable performance. For Bayesian network classifiers to become more widely accepted within the medical community then, we feel that they should be better aligned with their context of application. We describe how to incorporate well-known concepts of clinical relevance in the process of constructing and evaluating Bayesian network classifiers to achieve such an alignment.
