Diagnostic measurements for 768 women of Pima Indian heritage, aged at
least 21, together with whether diabetes was diagnosed within five years.
Five columns carry physiologically impossible zeros that are, in fact,
missing measurements; they are recoded to NA in this copy of the data,
as documented under Details.
Format
A data frame with 768 rows and 9 columns:
- Pregnancies
Number of times pregnant.
- Glucose
Plasma glucose concentration at 2 hours in an oral glucose tolerance test.
- BloodPressure
Diastolic blood pressure (mm Hg).
- SkinThickness
Triceps skinfold thickness (mm).
- Insulin
2-hour serum insulin (mu U/mL).
- BMI
Body mass index, weight in kg divided by height in m, squared.
- DiabetesPedigreeFunction
A score of diabetes likelihood based on family history.
- Age
Age in years.
- Outcome
Factor with levels
"no"and"yes": whether diabetes was diagnosed within five years.
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. Proceedings of the Symposium on Computer Applications and Medical Care, 261-265. Distributed by the UCI Machine Learning Repository and, from there, on Kaggle as https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database.
Details
Glucose, BloodPressure, SkinThickness, Insulin and BMI cannot be
zero in a living patient; a 0 recorded for any of them is a missing
measurement encoded as if it were data, a well-documented quirk of this
data set. Those zeros are recoded to NA here, the standard first
preprocessing step for it (see data-raw/pima_diabetes.R in the source
package for the exact recoding). Insulin is missing in 48.7% of records
and SkinThickness in 29.6%; only 51% of rows are complete on all five.
Whether a measurement is missing is not unrelated to the outcome; see
vignette("missing-values", package = "figsr") for a worked example using
na_method = "mia".
Examples
data(pima_diabetes)
summary(pima_diabetes)
#> Pregnancies Glucose BloodPressure SkinThickness
#> Min. : 0.000 Min. : 44.0 Min. : 24.00 Min. : 7.00
#> 1st Qu.: 1.000 1st Qu.: 99.0 1st Qu.: 64.00 1st Qu.:22.00
#> Median : 3.000 Median :117.0 Median : 72.00 Median :29.00
#> Mean : 3.845 Mean :121.7 Mean : 72.41 Mean :29.15
#> 3rd Qu.: 6.000 3rd Qu.:141.0 3rd Qu.: 80.00 3rd Qu.:36.00
#> Max. :17.000 Max. :199.0 Max. :122.00 Max. :99.00
#> NAs :5 NAs :35 NAs :227
#> Insulin BMI DiabetesPedigreeFunction Age
#> Min. : 14.00 Min. :18.20 Min. :0.0780 Min. :21.00
#> 1st Qu.: 76.25 1st Qu.:27.50 1st Qu.:0.2437 1st Qu.:24.00
#> Median :125.00 Median :32.30 Median :0.3725 Median :29.00
#> Mean :155.55 Mean :32.46 Mean :0.4719 Mean :33.24
#> 3rd Qu.:190.00 3rd Qu.:36.60 3rd Qu.:0.6262 3rd Qu.:41.00
#> Max. :846.00 Max. :67.10 Max. :2.4200 Max. :81.00
#> NAs :374 NAs :11
#> Outcome
#> no :500
#> yes:268
#>
#>
#>
#>
#>