Case-based reasoning support for liver disease diagnosis

TitleCase-based reasoning support for liver disease diagnosis
Publication TypeJournal Article
Year of Publication2011
Authors-LingChuang C
JournalArtificial Intelligence in Medicine
Volume53
Issue1
Start Page15
Pagination8
Date Published06/2011
ISSN0933-3657
KeywordsCase-based Reasoning, Hepatitis, Liver disease
Abstract

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),
classificationandregressiontree,logistic regression, anddiscriminatory analysis,inanattempttodevelop
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.

URLhttps://www.sciencedirect.com/science/article/pii/S0933365711000728