Aligning Bayesian Network Classifiers with Medical Contexts

Authors:
Linda C. Gaag;Silja Renooij;Ad Feelders;Arend Groote;Marinus J. Eijkemans;Frank J. Broekmans;Bart C. Fauser
Affiliations:
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands 3508 TB;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands 3508 TB;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands 3508 TB;Department of Reproductive Medicine and Gynaecology, Utrecht Medical Centre, Utrecht, The Netherlands 3584 CS;Department of Reproductive Medicine and Gynaecology, Utrecht Medical Centre, Utrecht, The Netherlands 3584 CS and Department of Public Health, Erasmus University Medical Center, Rotterdam, The Net ...;Department of Reproductive Medicine and Gynaecology, Utrecht Medical Centre, Utrecht, The Netherlands 3584 CS;Department of Reproductive Medicine and Gynaecology, Utrecht Medical Centre, Utrecht, The Netherlands 3584 CS
Venue:
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2009

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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, 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.