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Synthetic Diabetes Data

Source: R/data.R
pima.Rd

A fully synthetic diabetes data set created by Matthias Templ to mimic the Pima Indians diabetes data analyzed by Smith et al. (1988). Every value is synthetic and no row corresponds to a real person. The data were taken directly from mlbench::SynthDiabetes2, which mimics the missing-value pattern of the original data (physically impossible zeros are coded as NA).

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).

Details

Earlier versions of pdp shipped a copy of the original data (taken from mlbench::PimaIndiansDiabetes2) under this name. The original data had most likely been shared without the consent of the participants, and both the UCI repository and mlbench (as of version 2.1-11) have stopped distributing it. The pima name is kept so existing code continues to run, but results will differ from those based on the original data.

References

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 and Medical Care, 261-265.

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 (2026). mlbench: Machine Learning Benchmark Problems. R package version 2.1-11.

Examples

head(pima)
#>   pregnant glucose pressure triceps insulin mass pedigree age diabetes
#> 1        0     194       50      45      NA 28.5    0.240  28      neg
#> 2        8     129       84      NA      NA 27.6    0.828  44      pos
#> 3        2     132       72      42      NA 38.2    0.696  26      neg
#> 4        6     184       62      NA      NA 24.7    0.192  39      neg
#> 5        4     108       58      32      NA 31.6    0.178  22      neg
#> 6       10     117       80      13      NA 30.1    0.383  58      pos