liver: Liver
In tyluRp/ucimlr: UCI Machine Learning Repository
Description
Usage
Format
Details
Source
References
Description
The first 5 variables are all blood tests which are thought to be sensitive
to liver disorders that might arise from excessive alcohol consumption. Each
line in the dataset constitutes the record of a single male individual.
Usage
1
Format
A data frame with 345 observations on the following 7 variables.
mcv: mean corpuscular volume
alkphos: alkaline phosphotase
sgpt: alamine aminotransferase
sgot: aspartate aminotransferase
gammagt: gamma-glutamyl transpeptidase
drinks: number of half-pint equivalents of alcoholic beverages drunk per day
selector: field used to split data into two sets
Details
BUPA Medical Research Ltd. database donated by Richard S. Forsyth.
Important note: The 7th field (selector) has been widely misinterpreted in
the past as a dependent variable representing presence or absence of a liver
disorder. This is incorrect [1]. The 7th field was created by BUPA
researchers as a train/test selector. It is not suitable as a dependent
variable for classification. The dataset does not contain any variable
representing presence or absence of a liver disorder. Researchers who wish
to use this dataset as a classification benchmark should follow the method
used in experiments by the donor (Forsyth & Rada, 1986, Machine learning:
applications in expert systems and information retrieval) and others
(e.g. Turney, 1995, Cost-sensitive classification: Empirical evaluation of
a hybrid genetic decision tree induction algorithm), who used the 6th field
(drinks), after dichotomising, as a dependent variable for classification.
Because of widespread misinterpretation in the past, researchers should take
care to state their method clearly.
Source
Creators: BUPA Medical Research Ltd.
Donor: Richard S. Forsyth
8 Grosvenor Avenue
Mapperley Park
Nottingham NG3 5DX
0602-621676
Date: 5/15/1990
References
McDermott & Forsyth 2016, Diagnosing a disorder in a classification
benchmark, Pattern Recognition Letters, Volume 73.
https://archive.ics.uci.edu/ml/machine-learning-databases/liver-disorders/
https://archive.ics.uci.edu/ml/datasets/Liver+Disorders
tyluRp/ucimlr documentation built on Feb. 2, 2021, 6:51 a.m.
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Description Usage Format Details Source References
Description
The first 5 variables are all blood tests which are thought to be sensitive to liver disorders that might arise from excessive alcohol consumption. Each line in the dataset constitutes the record of a single male individual.
Usage
1 |
Format
A data frame with 345 observations on the following 7 variables.
mcv: mean corpuscular volume
alkphos: alkaline phosphotase
sgpt: alamine aminotransferase
sgot: aspartate aminotransferase
gammagt: gamma-glutamyl transpeptidase
drinks: number of half-pint equivalents of alcoholic beverages drunk per day
selector: field used to split data into two sets
Details
BUPA Medical Research Ltd. database donated by Richard S. Forsyth.
Important note: The 7th field (selector) has been widely misinterpreted in the past as a dependent variable representing presence or absence of a liver disorder. This is incorrect [1]. The 7th field was created by BUPA researchers as a train/test selector. It is not suitable as a dependent variable for classification. The dataset does not contain any variable representing presence or absence of a liver disorder. Researchers who wish to use this dataset as a classification benchmark should follow the method used in experiments by the donor (Forsyth & Rada, 1986, Machine learning: applications in expert systems and information retrieval) and others (e.g. Turney, 1995, Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm), who used the 6th field (drinks), after dichotomising, as a dependent variable for classification. Because of widespread misinterpretation in the past, researchers should take care to state their method clearly.
Source
Creators: BUPA Medical Research Ltd.
Donor: Richard S. Forsyth 8 Grosvenor Avenue Mapperley Park Nottingham NG3 5DX 0602-621676
Date: 5/15/1990
References
McDermott & Forsyth 2016, Diagnosing a disorder in a classification benchmark, Pattern Recognition Letters, Volume 73.
https://archive.ics.uci.edu/ml/machine-learning-databases/liver-disorders/
https://archive.ics.uci.edu/ml/datasets/Liver+Disorders
R Package Documentation
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