Case-based reasoning
Case-Based Reasoning Technology, From Foundations to Applications
Medical applications in case-based reasoning
The Knowledge Engineering Review
Integrations with case-based reasoning
The Knowledge Engineering Review
Machine learning in prognosis of the femoral neck fracture recovery
Artificial Intelligence in Medicine
Case-based reasoning in the health sciences: What's next?
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Uniqueness of medical data mining
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Evolutionary computing for knowledge discovery in medical diagnosis
Artificial Intelligence in Medicine
A hybrid diagnosis model for determining the types of the liver disease
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Case-based reasoning support for liver disease diagnosis
Artificial Intelligence in Medicine
An intelligent model for the classification of children's occupational therapy problems
Expert Systems with Applications: An International Journal
Improving the prediction accuracy of liver disorder disease with oversampling
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Objectives: Liver disease, the most common disease in Taiwan, is not easily discovered in its initial stage; early diagnosis of this leading cause of mortality is therefore highly important. The design of an effective diagnosis model is therefore an important issue in liver disease treatment. This study accordingly employs classification and regression tree (CART) and case-based reasoning (CBR) techniques to structure an intelligent diagnosis model aiming to provide a comprehensive analytic framework to raise the accuracy of liver disease diagnosis. Methods: Based on the advice and assistance of doctors and medical specialists of liver conditions, 510 outpatient visitors using ICD-9 (International Classification of Diseases, 9th Revision) codes at a medical center in Taiwan from 2005 to 2006 were selected as the cases in the data set for liver disease diagnosis. Data on 340 patients was utilized for the development of the model and on 170 patients utilized to perform comparative analysis of the models. This paper accordingly suggests an intelligent model for the diagnosis of liver diseases which integrates CART and CBR. The major steps in applying the model include: (1) adopting CART to diagnose whether a patient suffers from liver disease; (2) for patients diagnosed with liver disease in the first step, employing CBR to diagnose the types of liver diseases. Results: In the first phase, CART is used to extract rules from health examination data to show whether the patient suffers from liver disease. The results indicate that the CART rate of accuracy is 92.94%. In the second phase, CBR is developed to diagnose the type of liver disease, and the new case triggers the CBR system to retrieve the most similar case from the case base in order to support the treatment of liver disease. The new case is supported by a similarity ratio, and the CBR diagnostic accuracy rate is 90.00%. Actual implementation shows that the intelligent diagnosis model is capable of integrating CART and CBR techniques to examine liver diseases with considerable accuracy. The model can be used as a supporting system in making decisions regarding liver disease diagnosis and treatment. The rules extracted from CART are helpful to physicians in diagnosing liver diseases. CBR can retrieve the most similar case from the case base in order to solve a new liver disease problem and can be of great assistance to physicians in identifying the type of liver disease, reducing diagnostic errors and improving the quality and effectiveness of medical treatment.