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Data Mining and Liver Fibrosis: Single Stage and Multistage Classification Models for Noninvasive Prediction of Liver Fibrosis Degrees - Couverture souple

Hashem, Ahmed

 
9783659141041: Data Mining and Liver Fibrosis: Single Stage and Multistage Classification Models for Noninvasive Prediction of Liver Fibrosis Degrees
Couverture souple
ISBN 10 : 3659141046 ISBN 13 : 9783659141041
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Predicting significant fibrosis or cirrhosis in patients with hepatitis C virus has persistently preoccupied the research agenda of many specialized research centers. Many studies have been conducted to evaluate the use of readily available laboratory tests to predict significant fibrosis or cirrhosis with the purpose to substantially reduce the number of biopsies performed. Although many of them reported significant predictive values of several serum markers for the diagnosis of cirrhosis, none of these diagnostic techniques was successful in accurately predicting early stages of liver fibrosis. Therefore, in this study a single stage classification model and a multistage stepwise classification model based on Neural Network, Decision Tree, Logistic Regression, and Nearest Neighborhood clustering, have been developed to predict individual's liver fibrosis degree. Results showed that the area under the receiver operator curve (AUROC) values of the multistage model ranged from 0.874 to 0.974 which is a higher range than what is reported in current researches with similar conditions.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Predicting significant fibrosis or cirrhosis in patients with hepatitis C virus has persistently preoccupied the research agenda of many specialized research centers. Many studies have been conducted to evaluate the use of readily available laboratory tests to predict significant fibrosis or cirrhosis with the purpose to substantially reduce the number of biopsies performed. Although many of them reported significant predictive values of several serum markers for the diagnosis of cirrhosis, none of these diagnostic techniques was successful in accurately predicting early stages of liver fibrosis. Therefore, in this study a single stage classification model and a multistage stepwise classification model based on Neural Network, Decision Tree, Logistic Regression, and Nearest Neighborhood clustering, have been developed to predict individual's liver fibrosis degree. Results showed that the area under the receiver operator curve (AUROC) values of the multistage model ranged from 0.874 to 0.974 which is a higher range than what is reported in current researches with similar conditions.

Biographie de l'auteur

Dr. Ahmed Hashem has obtained his PhD degree in Biomedical Engineering in 2012. He is currently a Lecturer at Faculty of Engineering, Minia University, Egypt. He has worked in different international projects in the field of Data Mining. His research interests in developing noninvasive methods for the prediction of liver fibrosis degree.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3659141046
ISBN 13 
9783659141041
Reliure
Broché
Nombre de pages
272
Coordonnées du fabricant
non disponible
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