Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS

Authors:
Rosa Blanco;Iñaki Inza;Marisa Merino;Jorge Quiroga;Pedro Larrañaga
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Basque Country, San Sebastián, Spain;Department of Computer Science and Artificial Intelligence, University of Basque Country, San Sebastián, Spain;Basque Health Service-Osakidetza, Comarca Guipúzcoa-Este, San Sebastián, Spain;Faculty of Medicine, University Clinic of Navarra, Pamplona, Spain;Department of Computer Science and Artificial Intelligence, University of Basque Country, San Sebastián, Spain
Venue:
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Year:
2005

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Abstract

The transjugular intrahepatic portosystemic shunt (TIPS) is a treatment for cirrhotic patients with portal hypertension. A subgroup of patients dies in the first 6 months and another subgroup lives a long period of time. Nowadays, no risk factors have been identified in order to determine how long a patient will survive. An empirical study for predicting the survival rate within the first 6 months after TIPS placement is conducted using a clinical database with 107 cases and 77 variables. Applications of Bayesian classification models, based on Bayesian networks, to medical problems have become popular in the last years. Feature subset selection is useful due to the heterogeneity of the medical databases where not all the variables are required to perform the classification. In this paper, filter and wrapper approaches based on the feature subset selection are adapted to induce Bayesian classifiers (naive Bayes, selective naive Bayes, semi naive Bayes, tree augmented naive Bayes, and k-dependence Bayesian classifier) and are applied to distinguish between the two subgroups of cirrhotic patients. The estimated accuracies obtained tally with the results of previous studies. Moreover, the medical significance of the subset of variables selected by the classifiers along with the comprehensibility of Bayesian models is greatly appreciated by physicians.