Single stage and multistage classification models for the prediction of liver fibrosis degree in patients with chronic hepatitis C infection

Computer Methods and Programs in Biomedicine

Volumn 105, Issue 3, 2012, Pages 194-209

  Hashem, Ahmed M ^a   Rasmy, M Emad M ^b   Wahba, Khaled M ^b   Shaker, Olfat G ^c  

^a Minia University   (Egypt)

^b CAIRO UNIVERSITY   (Egypt)

^c CAIRO UNIVERSITY   (Egypt)

Author keywords

Hepatitis C virus; Liver fibrosis; Multistage classification; Pattern recognitions techniques

Indexed keywords

CHRONIC HEPATITIS; CLASSIFICATION MODELS; DIAGNOSTIC TECHNIQUES; HEPATITIS C VIRUS; LABORATORY TEST; LIVER FIBROSIS; LOGISTIC REGRESSIONS; MULTI-STAGE MODELS; NEAREST NEIGHBORHOOD; PREDICTIVE VALUES; RECEIVER OPERATOR CURVES; RESEARCH AGENDA; RESEARCH CENTER; SERUM MARKERS; SINGLE STAGE;

DECISION TREES; FORECASTING; NEURAL NETWORKS; RESEARCH; VIRUSES;

C (PROGRAMMING LANGUAGE);

ALANINE AMINOTRANSFERASE; ALBUMIN; ALKALINE PHOSPHATASE; ALPHA 2 MACROGLOBULIN; ALPHA FETOPROTEIN; APOLIPOPROTEIN A1; ASPARTATE AMINOTRANSFERASE; BILIRUBIN; BIOLOGICAL MARKER; CHOLESTEROL; CREATININE; GAMMA GLUTAMYLTRANSFERASE; GELATINASE A; GLUCOSE; HAPTOGLOBIN; HEMOGLOBIN; HYALURONIC ACID; IMMUNOGLOBULIN; INTERSTITIAL COLLAGENASE; PROTHROMBIN; SERUM ALBUMIN; THYROTROPIN; TISSUE INHIBITOR OF METALLOPROTEINASE 1; TRANSFORMING GROWTH FACTOR BETA1;

ADULT; ARTICLE; AUTOMATED PATTERN RECOGNITION; DISEASE CLASSIFICATION; DISEASE SEVERITY; ERYTHROCYTE COUNT; FEMALE; GLUCOSE BLOOD LEVEL; HEPATITIS C; HUMAN; INTERNATIONAL NORMALIZED RATIO; LABORATORY TEST; LEUKOCYTE COUNT; LIVER FIBROSIS; MAJOR CLINICAL STUDY; MALE; THROMBOCYTE; THROMBOCYTE COUNT;

ADULT; AREA UNDER CURVE; BIOLOGICAL MARKERS; FEMALE; HEPATITIS C, CHRONIC; HUMANS; LIVER; LIVER CIRRHOSIS; LOGISTIC MODELS; MALE; MIDDLE AGED; ROC CURVE;

EID: 84856224685     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.10.005     Document Type: Article

References (36)

1. El-Zanaty F. Ann Way, Egypt Demographic and Health Survey 2008 March 2009, Ministry of Health, El-Zanaty and Associates, and Macro International, Cairo, Egypt.
2. Saleh D.A., Shebl F., Abdel-Hamid M., et al. Incidence and risk factors for hepatitis C infection in a cohort of women in rural Egypt. Trans. R. Soc. Trop. Med. Hyg. 2008, 102(September (9)):921-928.
3. Gebo K., Herlong H., Torbenson M., et al. Role of liver biopsy in the management of chronic hepatitis C: a systematic review. Hepatology 2002, 36(November):161-172.
4. Cadranel J., Rufat P., Degos F. Practices of liver biopsy in France: results of a prospective nationwide survey. Hepatology 2000, 32(September (3)):477-481.
5. Regev A., Berho M., Jeffers L., Molina E., et al. Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection. Am. J. Gastroenterol. 2002, 9(October (10)):2614-2618.
6. Bedossa P., Dargère D. Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 2003, 38(December (6)):1499-1557.
7. Esmat G., Metwally M., Zalata K., et al. Evaluation of serum biomarkers of fibrosis and injury in Egyptian patients with chronic hepatitis C. Hepatology 2007, 46(April (4)):620-627.
8. Moemen M.E. Invasive versus non-invasive diagnosis of liver fibrosis (review article). Eg. J. Anaesth. 2008, 24:363-370.
9. Pinzani M., Vizzuttet F., Arena U., et al. Technology insight: noninvasive assessment of liver fibrosis by biochemical scores and elastography. Nat. Clin. Pract. Gastroenterol. Hepatol. 2008, 5(February (2)):95-106.
10. Montazeri G., Estakhri A., Mohamadnejad M., et al. Serum hyaluronate as a non-invasive marker of hepatic fibrosis and inflammation in HBeAg-negative chronic hepatitis B. BMC Gastroenterol. 2005, 5:32-38.
11. Sakugawa H., Nakayohshi T., Kobashigawa K., et al. Clinical usefulness of biochemical markers of liver fibrosis in patients with non alcoholic fatty liver disease. World J. Gastroenterol. 2005, 11:255-259.
12. Halfon P., Bourlière M., Pénaranda G., et al. Accuracy of hyaluronic acid level for predicting liver fibrosis stages in patients with hepatitis C virus. Compar. Hepatol. 2005, 4(July (6)):1-7.
13. Poynard T., Afdhal N.H. Perspectives on fibrosis progression in hepatitis C: an à la carte approach to risk factors and staging of fibrosis. Antivir. Ther. 2010, 15(3):281-291.
14. Alberti A., Clumeck N., Collins S., et al. Short statement of the first European Consensus Conference on the treatment of chronic hepatitis B and C in HIV co-infected patients. J. Hepatol. 2005, 42(May (5)):615-624.
15. Mayer B., Rangwala H., Gupta R., et al. Feature mining for prediction of degree of liver fibrosis. AMIA Annu. Symp. Proc. 2005, pp. 1048.
16. Cremilleux B., Durand N. Search for factors estimating the stage of liver fibrosis based on the discovery of meaningful clusters. PKDD 2002 Discovery Challenge on Hepatitis Data 2002.
17. Gorunescu F. Data mining techniques in computer-aided diagnosis: non-invasive cancer detection. World Acad. Sci. Eng. Technol. 2007, 34:280-283.
18. Resino S., Seoane J.A., Bellon J.M., et al. An artificial neural network improves the noninvasive diagnosis of significant fibrosis in HIV/HCV coinfected patients. J. Infect. 2011, 62(January (1)):77-86.
19. Wang D., Wang Q., Shan F., et al. Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers. BMC Infect. Dis. 2010, 10(August):251-258.
20. Dascalu G., Carausu E., Manuc D. Methods for data selection in medical databases: the binary logistic regression - relations with the calculated risks. World Acad. Sci. Eng. Technol. 2008, 38(February):278-282.
21. Stoean R., Preuss M., Stoean C., et al. An evolutionary approximation for the coefficients of decision functions within a support vector machine learning strategy. Found. Comput. Intell. 2009, 1:83-114.
22. Stoean R., Stoean C., Lupsor M., et al. Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C. Artif. Intell. Med. 2011, 51(1):53-65.
23. G. Mahajani, Y. Aslandogan, Evidence Combination in Medical Data Mining, Department of Computer Science and Engineering University of Texas at Arlington, U.S.A. Technical Report, 2003.
24. Abdel-Hamid M., Edelman D.C., Highsmith W.E., Constantine N.T. Optimization, assessment, and proposed use of a direct nested reverse transcription-polymerase chain reaction protocol for the detection of hepatitis C virus. J. Hum. Virol. 1997, 1(November (1)):58-65.
25. Bedossa P., Poynard T. An algorithm for the grading of activity in chronic hepatitis C, The METAVIR Cooperative Study Group. Hepatology 1996, 24(August (2)):289-293.
26. Allwein E., Schapire R., Singer Y. Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 2000, 1(December):113-141.
27. Groebner D., Shannon P., Fry P., Smith K. Business Statistics: A Decision Making Approach 2008, Prentice Hall, pp. 679-748.
28. Howes P., Crook N. Using input parameter influences to support the decisions of feedforward neural networks. Neurocomputing 2009, 24:191-206.
29. McHutchison J., Blatt L., de Medina M., et al. Measurement of serum hyaluronic acid in patients with chronic hepatitis C and its relationship to liver histology, Consensus Interferon Study Group. J. Gastroenterol. Hepatol. 2000, 15(August (8)):945-951.
30. Poynard T., Imbert-Bismut F., Ratziu V., Chevret S., Jardel C., et al. Biochemical markers of liver fibrosis in patients infected by hepatitis C virus: longitudinal validation in a randomized trial. J. Viral Hepatitis 2002, 9(March):128-133.
31. Imbert-Bismut F., Ratziu V., Pieroni L., Charlotte F., Benhamou Y., Poynard T. Biochemical markers of liver fibrosis in patients with hepatitis C virus infection: a prospective study. Lancet 2001, 357(April (9262)):1069-1075.
32. Bissell D.M., Roulot D., George J. Transforming growth factor beta and the liver. Hepatology 2001, 34(November (5)):859-867.
33. Friedman S.L. Molecular regulation of hepatic fibrosis, an integrated cellular response to tissue injury. J. Biol. Chem. 2000, 275(January (4)):2247-2250.
34. Murawaki Y., Ikuta Y., Kawasaki H. Clinical usefulness of serum tissue inhibitor of metalloproteinases (TIMP)-2 assay in patients with chronic liver disease in comparison with serum TIMP-1. Clin. Chim. Acta 1999, 281(March (2)):109-120.
35. Murphy F.R., Issa R., Zhou X., et al. Inhibition of apoptosis of activated hepatic stellate cells by tissue inhibitor of metalloproteinase-1 is mediated via effects on matrix metalloproteinase inhibition: implications for reversibility of liver fibrosis. J. Biol. Chem. 2002, 277(March (13)):11069-11076.
36. Afdhal N.H., Nunes D. Evaluation of liver fibrosis: a concise review. Am. J. Gastroenterol. 2004, 99(June (6)):1160-1174.