On this R-data statistics page, you will find information about the Pima.tr data set which pertains to Diabetes in Pima Indian Women. The Pima.tr data set is found in the MASS R package. You can load the Pima.tr data set in R by issuing the following command at the console data("Pima.tr"). This will load the data into a variable called Pima.tr. If R says the Pima.tr data set is not found, you can try installing the package by issuing this command install.packages("MASS") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the Pima.tr R data set. The size of this file is about 6,247 bytes.

Diabetes in Pima Indian Women

Description

A population of women who were at least 21 years old, of Pima Indian heritage and living near Phoenix, Arizona, was tested for diabetes according to World Health Organization criteria. The data were collected by the US National Institute of Diabetes and Digestive and Kidney Diseases. We used the 532 complete records after dropping the (mainly missing) data on serum insulin.

Usage

Pima.tr
Pima.tr2
Pima.te

Format

These data frames contains the following columns:

npreg

number of pregnancies.

glu

plasma glucose concentration in an oral glucose tolerance test.

bp

diastolic blood pressure (mm Hg).

skin

triceps skin fold thickness (mm).

bmi

body mass index (weight in kg/(height in m)\^2).

ped

diabetes pedigree function.

age

age in years.

type

Yes or No, for diabetic according to WHO criteria.

Details

The training set Pima.tr contains a randomly selected set of 200 subjects, and Pima.te contains the remaining 332 subjects. Pima.tr2 contains Pima.tr plus 100 subjects with missing values in the explanatory variables.

Source

Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C. and Johannes, R. S. (1988) Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications in Medical Care (Washington, 1988), ed. R. A. Greenes, pp. 261–265. Los Alamitos, CA: IEEE Computer Society Press.

Ripley, B.D. (1996) Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.

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Dataset imported from https://www.r-project.org.