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Pima Indians Diabetes Data

Source: R/data.R
pima.Rd

Diabetes test results collected by the the US National Institute of Diabetes and Digestive and Kidney Diseases from a population of women who were at least 21 years old, of Pima Indian heritage, and living near Phoenix, Arizona. The data were taken directly from mlbench::PimaIndiansDiabetes2().

Usage

data(pima)

Format

A data frame with 768 observations on 9 variables.

  • pregnant Number of times pregnant.

  • glucose Plasma glucose concentration (glucose tolerance test).

  • pressure Diastolic blood pressure (mm Hg).

  • triceps Triceps skin fold thickness (mm).

  • insulin 2-Hour serum insulin (mu U/ml).

  • mass Body mass index (weight in kg/(height in m)^2).

  • pedigree Diabetes pedigree function.

  • age Age (years).

  • diabetes Factor indicating the diabetes test result (neg/pos).

References

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases http://www.ics.uci.edu/~mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science.

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

Grace Whaba, Chong Gu, Yuedong Wang, and Richard Chappell (1995), Soft Classification a.k.a. Risk Estimation via Penalized Log Likelihood and Smoothing Spline Analysis of Variance, in D. H. Wolpert (1995), The Mathematics of Generalization, 331-359, Addison-Wesley, Reading, MA.

Friedrich Leisch & Evgenia Dimitriadou (2010). mlbench: Machine Learning Benchmark Problems. R package version 2.1-1.

Examples

head(pima)
#>   pregnant glucose pressure triceps insulin mass pedigree age diabetes
#> 1        6     148       72      35      NA 33.6    0.627  50      pos
#> 2        1      85       66      29      NA 26.6    0.351  31      neg
#> 3        8     183       64      NA      NA 23.3    0.672  32      pos
#> 4        1      89       66      23      94 28.1    0.167  21      neg
#> 5        0     137       40      35     168 43.1    2.288  33      pos
#> 6        5     116       74      NA      NA 25.6    0.201  30      neg