KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

KEEL-dataset - data set description

Hepatitis data set

Description
Files and additional references

Description

This section describes main characteristics of the hepatitis data set and its attributes:

General information

Hepatitis data set
Type	Classification	Origin	Real world
Features	19	(Real / Integer / Nominal)	(2 / 17 / 0)
Classes	2	Missing values?	Yes
Total instances	155	Instances without missing values	80

Attribute description

Attribute	Domain	Attribute	Domain
Age	[7, 78]	Spiders	[1, 2]
Sex	[1, 2]	Ascites	[1, 2]
Steroid	[1, 2]	Varices	[1, 2]
Antivirals	[1, 2]	Bilirubin	[0.3, 8.0]
Fatigue	[1, 2]	AlkPhosphate	[26, 295]
Malaise	[1, 2]	Sgot	[14, 648]
Anorexia	[1, 2]	AlbuMin	[2.1, 6.4]
LiverBig	[1, 2]	ProTime	[0, 100]
LiverFirm	[1, 2]	Histology	[1, 2]
SpleenPalpable	[1, 2]	Class	{1, 2}

Additional information

This data set contains a mixture of integer and real valued attributes, with information about patients affected by the Hepatitis disease.

The task is to predict if these patients will die (1) or survive (2).

Files and additional references

In this section you can download some files related to the hepatitis data set:

The complete data set already formatted in KEEL formatcan be downloaded from here .
A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from here .
A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from here .
The header file associated to this data set can be downloaded from here .
This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Hepatitis.