Feature Subset Selection Using Probabilistic Tree Structures. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS

The transjugular intrahepatic portosystemic shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staff's experience, the consequences of the TIPS are not homogeneous for all the patients and a subgroup of them dies in the first six months after the TIPS placement. Actually, there is no risk indicator to identify this group, before treatment. An investigation for predicting the survival of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. Naive-Bayes, C4.5 and CN2 supervised classifiers are applied to identify this group. The application of several Feature Subset Selection (FSS) techniques has significantly improved the predictive accuracy of these classifiers and considerably reduced the amount of attributes in the classification models. Among FSS techniques, FSS-TREE, a new randomized algorithm inspired on the EDA (Estimation of Distribution Algorithm) paradigm, has obtained the best accuracy results.

[1]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[2]  Bojan Cestnik,et al.  Estimating Probabilities: A Crucial Task in Machine Learning , 1990, ECAI.

[3]  G. D’Amico,et al.  The treatment of portal hypertension: A meta‐analytic review , 1995, Hepatology.

[4]  P. Kamath,et al.  A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts , 2000, Hepatology.

[5]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[6]  W Gerok,et al.  NEW NON-OPERATIVE TREATMENT FOR VARICEAL HAEMORRHAGE , 1989, The Lancet.

[7]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[9]  Kenneth DeJong,et al.  Robust feature selection algorithms , 1993, Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93).

[10]  Nir Friedman,et al.  On the Sample Complexity of Learning Bayesian Networks , 1996, UAI.

[11]  R. Pugh,et al.  Transection of the oesophagus for bleeding oesophageal varices , 1973, The British journal of surgery.

[12]  Chi Hau Chen,et al.  Pattern recognition and signal processing , 1978 .

[13]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[14]  A Ochs,et al.  The first decade of the transjugular intrahepatic portosystemic shunt (TIPS): state of the art. , 2008, Liver.

[15]  Jerzy Stefanowski,et al.  Feature subset selection for classification of histological images , 1997, Artif. Intell. Medicine.

[16]  J. Krige,et al.  Management of oesophageal varices , 1994, The Lancet.

[17]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[18]  D. Michie.  Personal models of rationality , 1990 .

[19]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[20]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.

[21]  David E. Goldberg,et al.  Linkage Problem, Distribution Estimation, and Bayesian Networks , 2000, Evolutionary Computation.

[22]  H O Conn,et al.  A peek at the child‐turcotte classification , 1981, Hepatology.

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[24]  D. Shafritz,et al.  Identification of integrated hepatitis B virus DNA sequences in human hepatocellular carcinomas , 1981, Hepatology.

[25]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..

[26]  Dimitris Fouskakis,et al.  A Case Study of Stochastic Optimization in Health Policy: Problem Formulation and Preliminary Results , 2000, J. Glob. Optim..

[27]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[28]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[29]  J. Kittler,et al.  Feature Set Search Alborithms , 1978 .

[30]  W. Hislop,et al.  A 20-year prospective study of cirrhosis. , 1981, British medical journal.

[31]  Constantin F. Aliferis,et al.  An evaluation of machine-learning methods for predicting pneumonia mortality , 1997, Artif. Intell. Medicine.