Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Case-based reasoning
Case-Based Reasoning Technology, From Foundations to Applications
Model selection for medical diagnosis decision support systems
Decision Support Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An intelligent model for liver disease diagnosis
Artificial Intelligence in Medicine
Case-based reasoning in the health sciences: What's next?
Artificial Intelligence in Medicine
Computational Statistics & Data Analysis
A hybrid diagnosis model for determining the types of the liver disease
Computers in Biology and Medicine
Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction
Information Sciences: an International Journal
Early prediction of the highest workload in incremental cardiopulmonary tests
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Engineering Applications of Artificial Intelligence
International Journal of Systems Biology and Biomedical Technologies
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Objectives: In Taiwan, as well as in the other countries around the world, liver disease has reigned over the list of leading causes of mortality, and its resistance to early detection renders the disease even more threatening. It is therefore crucial to develop an auxiliary system for diagnosing liver disease so as to enhance the efficiency of medical diagnosis and to expedite the delivery of proper medical treatment. Methods: The study accordingly integrated the case-based reasoning (CBR) model into several common classification methods of data mining techniques, including back-propagation neural network (BPN), classification and regression tree, logistic regression, and discriminatory analysis, in an attempt to develop a more efficient model for early diagnosis of liver disease and to enhance classification accuracy. To minimize possible bias, this study used a ten-fold cross-validation to select a best model for more precise diagnosis results and to reduce problems caused by false diagnosis. Results: Through a comparison of five single models, BPN and CBR emerged to be the top two methods in terms of overall performance. For enhancing diagnosis performance, CBR was integrated with other methods, and the results indicated that the accuracy and sensitivity of each CBR-added hybrid model were higher than those of each single model. Of all the CBR-added hybrid models, the BPN-CBR method took the lead in terms of diagnosis capacity with an accuracy rate of 95%, a sensitivity of 98%, and a specificity of 94%. Conclusions: After comparing the five single and hybrid models, the study found BPN-CBR the best model capable of helping physicians to determine the existence of liver disease, achieve an accurate diagnosis, diminish the possibility of a false diagnosis being given to sick people, and avoid the delay of clinical treatment.