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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