Statistical Appraisal of the Pima Indians Diabetes Dataset: A Review and Empirical Re-analysis of Risk Factors and Predictive Models

Abstract

Diabetes mellitus is a major chronic disease, and statistical risk modelling of its onset has been studied for
several decades. The Pima Indians Diabetes Database, comprising 768 women aged 21 years and above, has
become one of the most frequently reused benchmark datasets in the biomedical statistics and machine
learning (ML) literature, yet the statistical properties of the data and the validity of results derived from it are
rarely examined together. This review combines a narrative synthesis of the literature with a full statistical
re-analysis of the supplied dataset (268 diabetic, 500 non-diabetic; prevalence 34.9%). We examine data
quality, descriptive and distributional properties, univariate group comparisons with effect sizes, categorical
association tests, correlation structure, multivariable logistic regression and a ten-fold cross-validated
comparison of seven classifiers. Our synthesis finds that (i) five physiological variables encode missing
measurements as zero, so that only 392 of 768 records (51.0%) are complete; (ii) all eight predictors differ
significantly between groups (Benjamini-Hochberg adjusted p < 0.001), with plasma glucose showing by far
the largest standardised effect (Cohen’s d = 1.19); (iii) after mutual adjustment, only glucose, BMI, number of
pregnancies and the diabetes pedigree function remain independent predictors (McFadden R2 = 0.282); and
(iv) seven classifiers achieve cross-validated AUCs of 0.813-0.837 and accuracies of 74.6%-77.2%, with no
model significantly better than plain logistic regression. We propose a short reporting checklist for studies
using this dataset and identify six priority research directions.