Context
The data set contains laboratory values of blood donors and Hepatitis C patients and demographic values like age. The data was obtained from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/HCV+data
Content
All attributes except Category and Sex are numerical.
Attributes 1 to 4 refer to the data of the patient:
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)
Attributes 5 to 14 refer to laboratory data:
5) ALB
6) ALP
7) ALT
8) AST
9) BIL
10) CHE
11) CHOL
12) CREA
13) GGT
14) PROT
The target attribute for classification is Category (2): blood donors vs. Hepatitis C patients (including its progress ('just' Hepatitis C, Fibrosis, Cirrhosis).
Acknowledgements
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
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 case study. J Lab Precis Med 2018; 3: 58-67
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