Data Set Name: Hepatocellular Carcinoma Dataset (HCC dataset)

Abstract: Hepatocellular Carcinoma dataset (HCC dataset) was collected at a University Hospital in Portugal. It contains real clinical data of 165 patients diagnosed with HCC.

Donors:
Miriam Seoane Santos (miriams@student.dei.uc.pt) and Pedro Henriques Abreu (pha@dei.uc.pt), Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra
Armando Carvalho (aspcarvalho@gmail.com) and Adélia Simão (adeliasimao@gmail.com), Internal Medicine Service, Hospital and University Centre of Coimbra

Data Type: Multivariate
Task: Classification, Regression, Clustering, Casual Discovery
Attribute Type: Categorical, Integer and Real

Area: Life Sciences
Format Type: Matrix
Missing values: Yes

Instances and Attributes:
Number of Instances (records in your data set): 165
Number of attributes (fields within each record): 49

Relevant Information:
HCC dataset was obtained at a University Hospital in Portugal and contais several demographic, risk factors, laboratory and overall survival features of 165 real patients diagnosed with HCC. The dataset contains 49 features selected according to the EASL-EORTC (European Association for the Study of the Liver - European Organisation for Research and Treatment of Cancer) Clinical Practice Guidelines, which are the current state-of-the-art on the management of HCC.

This is an heterogeneous dataset, with 23 quantitative variables, and 26 qualitative variables. Overall, missing data represents 10.22% of the whole dataset and only eight patients have complete information in all fields (4.85%). The target variables is the survival at 1 year, and was encoded as a binary variable: 0 (dies) and 1 (lives). A certain degree of class-imbalance is also present (63 cases labeled as “dies” and 102 as “lives”).

A detailed description of the HCC dataset (feature’s type/scale, range, mean/mode and missing data percentages) is provided in Santos et al. “A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients”, Journal of biomedical informatics, 58, 49-59, 2015.