Case-based reasoning support for liver disease diagnosis

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.

[1]  Dansheng Song,et al.  Ipsilateral-mammogram computer-aided detection of breast cancer. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[2]  Giuseppe Rocca,et al.  Predictors of psychiatric disorders in liver transplantation candidates: Logistic regression models , 2003, Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society.

[3]  P. Vik,et al.  Transition to College: Α Classification and Regression Tree (CART) analysis of natural reduction of binge drinking , 2006, International journal of adolescent medicine and health.

[4]  Johan A. K. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..

[5]  Simona Rossi,et al.  Development of an HCV infection risk stratification algorithm for patients on chronic hemodialysis. , 2002 .

[6]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[7]  N. Min,et al.  Electrochemical biosensor array for liver diagnosis using a silanization technique on nano-porous silicon electrode , 2006, 2006 IEEE Nanotechnology Materials and Devices Conference.

[8]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[9]  Patrice Degoulet,et al.  Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression , 2000, AMIA.

[10]  Paola Gramatica,et al.  Quantitative structure-activity relationship modeling of polycyclic aromatic hydrocarbon mutagenicity by classification methods based on holistic theoretical molecular descriptors. , 2007, Ecotoxicology and environmental safety.

[11]  S. Goodacre,et al.  How do clinical features help identify paediatric patients with fractures following blunt wrist trauma? , 2006, Emergency Medicine Journal.

[12]  P. Wilding,et al.  Application of neural networks to the interpretation of laboratory data in cancer diagnosis. , 1992, Clinical chemistry.

[13]  Robert P. W. Duin,et al.  Bagging, Boosting and the Random Subspace Method for Linear Classifiers , 2002, Pattern Analysis & Applications.

[14]  W. Baxt.  Use of an artificial neural network for the diagnosis of myocardial infarction. , 1991, Annals of internal medicine.

[15]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[16]  A. Lonardo,et al.  Differential diagnosis between hepatic cirrhosis and chronic active hepatitis: specificity and sensitivity of physical and laboratory findings in a series from the Mediterranean area. , 1983, The American journal of gastroenterology.

[17]  U Ranft,et al.  Random field models in the textural analysis of ultrasonic images of the liver , 1996, IEEE Trans. Medical Imaging.

[18]  P. Bedossa,et al.  Ultrasonographic diagnosis of hepatic fibrosis or cirrhosis. , 1999, Journal of hepatology.

[19]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[20]  Leonid Churilov,et al.  Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners , 2009, Eur. J. Oper. Res..

[21]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[22]  Chun-Ling Chuang,et al.  A hybrid diagnosis model for determining the types of the liver disease , 2010, Comput. Biol. Medicine.

[23]  W. Lee,et al.  Biliary cystadenoma of the liver. , 1998, Journal of hepato-biliary-pancreatic surgery.

[24]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[25]  Ming-Huwi Horng,et al.  An ultrasonic image evaluation system for assessing the severity of chronic liver disease , 2007, Comput. Medical Imaging Graph..

[26]  M Yearworth,et al.  Artificial neural networks in diagnosis of thyroid function from in vitro laboratory tests. , 1993, Clinical chemistry.

[27]  Isabelle Bichindaritz,et al.  Case-based reasoning in the health sciences: What's next? , 2006, Artif. Intell. Medicine.

[28]  Rainer Schmidt,et al.  CBR in Medicine , 1998, Case-Based Reasoning Technology.

[29]  Rong-Ho Lin,et al.  An intelligent model for liver disease diagnosis , 2009, Artif. Intell. Medicine.

[30]  David West,et al.  Model selection for medical diagnosis decision support systems , 2004, Decis. Support Syst..

[31]  Anil Varma,et al.  ICARUS: design and deployment of a case-based reasoning system for locomotive diagnostics , 1999 .

[32]  Philip J Day,et al.  Artificial neural networks and decision tree model analysis of liver cancer proteomes. , 2007, Biochemical and biophysical research communications.

[33]  Alessandra Orlandini,et al.  Prediction of significant fibrosis in chronic hepatitis C patients by artificial neural network analysis of clinical factors , 2007 .

[34]  G DietterichThomas.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees , 2000 .

[35]  Hiroyuki Kaneda,et al.  Nonalcoholic steatohepatitis: risk factors for liver fibrosis. , 2002, Hepatology research : the official journal of the Japan Society of Hepatology.

[36]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[37]  Ingoo Han,et al.  A case-based reasoning system with the two-dimensional reduction technique for customer classification , 2007, Expert Syst. Appl..

[38]  T Groth,et al.  Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis. , 1997, Clinical chemistry.

[39]  J. Ferlay,et al.  Global Cancer Statistics, 2002 , 2005, CA: a cancer journal for clinicians.

[40]  Ingoo Han,et al.  Risk analysis for electronic commerce using case-based reasoning , 1999 .

[41]  Stephanie M. Bryant,et al.  A case-based reasoning approach to bankruptcy prediction modeling , 1996 .

[42]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[43]  Benjamin W Sears,et al.  Clinician vs mathematical statistical models: which is better at predicting an abnormal chest radiograph finding in injured patients? , 2007, The American journal of emergency medicine.

[44]  B Carstensen,et al.  Accuracy of clinical diagnosis of cirrhosis among alcohol-abusing men. , 1996, Journal of clinical epidemiology.

[45]  Tian-Shyug Lee,et al.  Mining the customer credit using classification and regression tree and multivariate adaptive regression splines , 2006, Comput. Stat. Data Anal..

[46]  R. Cozzolongo,et al.  Artificial neural networks for the prediction of response to interferon plus ribavirin treatment in patients with chronic hepatitis C. , 2004, Current pharmaceutical design.

[47]  Mevlut Ture,et al.  Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease , 2008, Expert Syst. Appl..

[48]  Patrice Faure,et al.  Prospective comparison of six non-invasive scores for the diagnosis of liver fibrosis in chronic hepatitis C. , 2007, Journal of hepatology.

[49]  Ingoo Han,et al.  Case-based reasoning supported by genetic algorithms for corporate bond rating , 1999 .

[50]  C. R. Mount,et al.  A case-based reasoning system for identifying failure mechanisms , 2000 .

[51]  Bo-Suk Yang,et al.  Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis , 2004, Expert Syst. Appl..

[52]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[53]  Yoshihiro Fukuda,et al.  Lymphocyte-mediated enhancement of hepatic fibrosis in chronic active hepatitis , 1985, Gastroenterologia Japonica.