title: A hybrid diagnosis model for determining the types of the liver disease.
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.
authors.affiliation: Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao East Road, Taipei 10608, Taiwan, ROC. rhlin@ntut.edu.tw
authors.forename: Rong-Ho
authors.initials: RH
authors.lastname: Lin
authors.forename: Chun-Ling
authors.initials: CL
authors.lastname: Chuang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006508:Hepatitis Antibodies; D018963:Hepatitis Antigens; D006801:Humans; D008107:Liver Diseases / Q000175:diagnosis*; D008111:Liver Function Tests; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer; D012307:Risk Factors
chemical_list: D006508:Hepatitis Antibodies; D018963:Hepatitis Antigens