丙型肝炎、纤维化、肝硬化数据集

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