title: Justified granulation aided noninvasive liver fibrosis classification system.
abstract: BACKGROUND
  According to the World Health Organization 130-150 million (according to WHO) of people globally are chronically infected with hepatitis C virus. The virus is responsible for chronic hepatitis that ultimately may cause liver cirrhosis and death. The disease is progressive, however antiviral treatment may slow down or stop its development. Therefore, it is important to estimate the severity of liver fibrosis for diagnostic, therapeutic and prognostic purposes. Liver biopsy provides a high accuracy diagnosis, however it is painful and invasive procedure. Recently, we witness an outburst of non-invasive tests (biological and physical ones) aiming to define severity of liver fibrosis, but commonly used FibroTest®, according to an independent research, in some cases may have accuracy lower than 50 %. In this paper a data mining and classification technique is proposed to determine the stage of liver fibrosis using easily accessible laboratory data.
  
  
  METHODS
  Research was carried out on archival records of routine laboratory blood tests (morphology, coagulation, biochemistry, protein electrophoresis) and histopathology records of liver biopsy as a reference value. As a result, the granular model was proposed, that contains a series of intervals representing influence of separate blood attributes on liver fibrosis stage. The model determines final diagnosis for a patient using aggregation method and voting procedure. The proposed solution is robust to missing or corrupted data.
  
  
  RESULTS
  The results were obtained on data from 290 patients with hepatitis C virus collected over 6 years. The model has been validated using training and test data. The overall accuracy of the solution is equal to 67.9 %. The intermediate liver fibrosis stages are hard to distinguish, due to effectiveness of biopsy itself. Additionally, the method was verified against dataset obtained from 365 patients with liver disease of various etiologies. The model proved to be robust to new data. What is worth mentioning, the error rate in misclassification of the first stage and the last stage is below 6.5 % for all analyzed datasets.
  
  
  CONCLUSIONS
  The proposed system supports the physician and defines the stage of liver fibrosis in chronic hepatitis C. The biggest advantage of the solution is a human-centric approach using intervals, which can be verified by a specialist, before giving the final decision. Moreover, it is robust to missing data. The system can be used as a powerful support tool for diagnosis in real treatment.
authors.affiliation: Institute of Computer Science, Faculty of Computer Science and Material Science, University of Silesia in Katowice, Katowice, Poland. marcin.bernas@us.edu.pl.
authors.forename: Marcin
authors.initials: M
authors.lastname: Bernas
authors.affiliation: Institute of Computer Science, Faculty of Computer Science and Material Science, University of Silesia in Katowice, Katowice, Poland.
authors.forename: Tomasz
authors.initials: T
authors.lastname: Orczyk
authors.affiliation: Department of Basic Biomedical Science, School of Pharmacy with Division of Laboratory Medicine in Sosnowiec, Medical University of Silesia in Katowice, Katowice, Poland.|Department of Gastroenterology and Hepatology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland.
authors.forename: Joanna
authors.initials: J
authors.lastname: Musialik
authors.affiliation: Department of Gastroenterology and Hepatology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland.
authors.forename: Marek
authors.initials: M
authors.lastname: Hartleb
authors.affiliation: Department of Basic Biomedical Science, School of Pharmacy with Division of Laboratory Medicine in Sosnowiec, Medical University of Silesia in Katowice, Katowice, Poland.
authors.forename: Barbara
authors.initials: B
authors.lastname: Błońska-Fajfrowska
mesh_terms: D000328:Adult; D000368:Aged; D057225:Data Mining; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D020407:Internet; D008103:Liver Cirrhosis / Q000145:classification* / Q000175:diagnosis*; D008491:Medical Informatics Applications; D008875:Middle Aged; D012720:Severity of Illness Index