Rosa Blanco | PubMed Health

PubMed2005

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

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.

Bayes Theorem↗

PubMed2004

Filter versus wrapper gene selection approaches in DNA microarray domains.

DNA microarray experiments generating thousands of gene expression measurements, are used to collect information from tissue and cell samples regarding gene expression differences that could be useful for diagnosis disease, distinction of the specific tumor type, etc. One important application of gene expression microarray data is the classification of samples into known categories. As DNA microarray technology measures the gene expression en masse, this has resulted in data with the number of features (genes) far exceeding the number of samples. As the predictive accuracy of supervised classifiers that try to discriminate between the classes of the problem decays with the existence of irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. We propose the application of a gene selection process, which also enables the biology researcher to focus on promising gene candidates that actively contribute to classification in these large scale microarrays. Two basic approaches for feature selection appear in machine learning and pattern recognition literature: the filter and wrapper techniques. Filter procedures are used in most of the works in the area of DNA microarrays. In this work, a comparison between a group of different filter metrics and a wrapper sequential search procedure is carried out. The comparison is performed in two well-known DNA microarray datasets by the use of four classic supervised classifiers. The study is carried out over the original-continuous and three-intervals discretized gene expression data. While two well-known filter metrics are proposed for continuous data, four classic filter measures are used over discretized data. The same wrapper approach is used for both continuous and discretized data. The application of filter and wrapper gene selection procedures leads to considerably better accuracy results in comparison to the non-gene selection approach, coupled with interesting and notable dimensionality reductions. Although the wrapper approach mainly shows a more accurate behavior than filter metrics, this improvement is coupled with considerable computer-load necessities. We note that most of the genes selected by proposed filter and wrapper procedures in discrete and continuous microarray data appear in the lists of relevant-informative genes detected by previous studies over these datasets. The aim of this work is to make contributions in the field of the gene selection task in DNA microarray datasets. By an extensive comparison with more popular filter techniques, we would like to make contributions in the expansion and study of the wrapper approach in this type of domains.

Artificial Intelligence↗