Nearly half of a widely used diabetes symptom dataset repeats itself: grouping by profile drops ensemble accuracy from 98% to 90–93%
In the UCI Early-Stage Diabetes dataset, 269 of 520 questionnaire records repeat another record's full predictor vector, and row-level cross-validation splits copies of one profile across training and test folds. Grouping all records of a profile into the same fold lowered the Matthews correlation coefficient of ten ensembles by 0.105 to 0.157 and accuracy from 97.2–98.2% to 90.2–93.2%, and changed model rankings. Grouping near-duplicate symptom patterns that differ only in age lowered performance further for every model.