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Diabetes health indicators dataset, used to compare classic machine learning and quantum machine learning techniques for feature selection and classification.

The dataset comprises information on 442 diabetes patients and is used for predicting disease progression. It originates from the scikit‑learn library and includes features relevant to disease progression forecasting.

This dataset contains various health‑related features and a binary target variable indicating the presence or absence of diabetes. The dataset originates from the CDC and is used to build machine‑learning models to classify individuals as diabetic or not.

The project uses the Pima Indians Diabetes Database from the UCI Machine Learning Repository. The dataset includes several medical predictor variables and a target variable, Outcome, indicating whether a patient has diabetes (1) or not (0).