Hepatitis-C-Prediction-Dataset
OpenML dataset with id 43797
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102622/Hepatitis-C-Prediction-Dataset.arff
Upload date: 24 March 2022
Dataset Characteristics
Number of features: 14 (numeric: 12, symbolic: 0 and in total binary: 0 )
Number of instances: 615
Number of instances with missing values: 26
Number of missing values: 31
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 Other Datasets
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