Creators: Ralf Lichtinghagen, Frank Klawonn, Georg Hoffmann
Donor: Ralf Lichtinghagen: Institute of Clinical Chemistry; Medical University Hannover (MHH); Hannover, Germany; lichtinghagen.ralf '@' mh-hannover.de
Donor: Frank Klawonn; Helmholtz Centre for Infection Research; Braunschweig, Germany; frank.klawonn '@' helmholtz-hzi.de
Donor: Georg Hoffmann; Trillium GmbH; Grafrath, Germany; georg.hoffmann '@' trillium.de

Data Set Information:

The target attribute for classification is Category (blood donors vs. Hepatitis C (including its progress ('just' Hepatitis C, Fibrosis, Cirrhosis).

Attribute Information:

All attributes except Category and Sex are numerical. The laboratory data are the attributes 5-14.
1) X (Patient ID/No.)
2) Category (diagnosis) (values: '0=Blood Donor', '0s=suspect Blood Donor', '1=Hepatitis', '2=Fibrosis', '3=Cirrhosis')
3) Age (in years)
4) Sex (f,m)
5) ALB
6) ALP
7) ALT
8) AST
9) BIL
10) CHE
11) CHOL
12) CREA
13) GGT
14) PROT

Relevant Papers:

Lichtinghagen R et al. J Hepatol 2013; 59: 236-42
Hoffmann G et al. Using machine learning techniques to generate laboratory diagnostic pathways a€“ a case study. J Lab Precis Med 2018; 3: 58-67

Citation Request:

Please refer to the Machine Learning Repository's citation policy