Literature DB >> 20591425

A hybrid diagnosis model for determining the types of the liver disease.

Rong-Ho Lin1, Chun-Ling Chuang.   

Abstract

The symptoms of liver diseases are not apparent in the initial stage, and the condition is usually quite serious when the symptoms are obvious enough. Most studies on liver disease diagnosis focus mainly on identifying the presence of liver disease in a patient. Not many diagnosis models have been developed to move beyond the detection of liver disease. The study accordingly aims to construct an intelligent liver diagnosis model which integrates artificial neural networks, analytic hierarchy process, and case-based reasoning methods to examine if patients suffer from liver disease and to determine the types of the liver disease. Copyright 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20591425     DOI: 10.1016/j.compbiomed.2010.06.002

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  4 in total

Review 1.  Role of Soft Computing Approaches in HealthCare Domain: A Mini Review.

Authors:  Shalini Gambhir; Sanjay Kumar Malik; Yugal Kumar
Journal:  J Med Syst       Date:  2016-10-29       Impact factor: 4.460

2.  A New Intelligent Medical Decision Support System Based on Enhanced Hierarchical Clustering and Random Decision Forest for the Classification of Alcoholic Liver Damage, Primary Hepatoma, Liver Cirrhosis, and Cholelithiasis.

Authors:  Aman Singh; Babita Pandey
Journal:  J Healthc Eng       Date:  2018-02-01       Impact factor: 2.682

3.  Artificial neural networks modeling gene-environment interaction.

Authors:  Frauke Günther; Iris Pigeot; Karin Bammann
Journal:  BMC Genet       Date:  2012-05-14       Impact factor: 2.797

Review 4.  Applying the Analytic Hierarchy Process in healthcare research: A systematic literature review and evaluation of reporting.

Authors:  Katharina Schmidt; Ines Aumann; Ines Hollander; Kathrin Damm; J-Matthias Graf von der Schulenburg
Journal:  BMC Med Inform Decis Mak       Date:  2015-12-24       Impact factor: 2.796

  4 in total

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