Data
coronary_dataset

coronary_dataset

active ARFF Publicly available Visibility: public Uploaded 31-01-2022 by Oleksandr Zadorozhnyi
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  • Computer Systems Machine Learning
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Dataset description Probable risk factors for coronary thrombosis, comprising data from 1841 men. Format of the dataset The coronary data set contains the following 6 variables: Smoking (smoking): a two-level factor with levels no and yes. M. Work (strenuous mental work): a two-level factor with levels no and yes. P. Work (strenuous physical work): a two-level factor with levels no and yes. Pressure (systolic blood pressure): a two-level factor with levels <140 and >140. Proteins (ratio of beta and alpha lipoproteins): a two-level factor with levels <3 and >3. Family (family anamnesis of coronary heart disease): a two-level factor with levels neg and pos. Source Edwards DI (2000). Introduction to Graphical Modelling. Springer, 2nd edition. Reinis Z, Pokorny J, Basika V, Tiserova J, Gorican K, Horakova D, Stuchlikova E, Havranek T, Hrabovsky F (1981). "Prognostic Significance of the Risk Profile in the Prevention of Coronary Heart Disease". Bratisl Lek Listy, 76:137-150. Published on Bratislava Medical Journal, in Czech. Whittaker J (1990). Graphical Models in Applied Multivariate Statistics. Wiley.

6 features

Smokingnominal2 unique values
0 missing
M. Worknominal2 unique values
0 missing
P. Worknominal2 unique values
0 missing
Pressurenominal2 unique values
0 missing
Proteinsnominal2 unique values
0 missing
Familynominal2 unique values
0 missing

19 properties

1841
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
6
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
100
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
6
Number of binary attributes.
100
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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