1. SPECTF Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.
2. seeds: Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes.
3. Pima Indians Diabetes: From National Institute of Diabetes and Digestive and Kidney Diseases; Includes cost data (donated by Peter Turney)
4. Parkinsons: Oxford Parkinson's Disease Detection Dataset
5. MicroMass: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data.
6. Mammographic Mass: Discrimination of benign and malignant mammographic masses based on BI-RADS attributes and the patient's age.
7. Iris: Famous database; from Fisher, 1936
8. Haberman's Survival: Dataset contains cases from study conducted on the survival of patients who had undergone surgery for breast cancer
9. Ecoli: This data contains protein localization sites
10. Daphnet Freezing of Gait: This dataset contains the annotated readings of 3 acceleration sensors at the hip and leg of Parkinson's disease patients that experience freezing of gait (FoG) during walking tasks.
11. Breast Tissue: Dataset with electrical impedance measurements of freshly excised tissue samples from the breast.
12. Breast Cancer Wisconsin (Prognostic): Prognostic Wisconsin Breast Cancer Database
13. Breast Cancer Wisconsin (Original): Original Wisconsin Breast Cancer Database
14. Breast Cancer Wisconsin (Diagnostic): Diagnostic Wisconsin Breast Cancer Database
15. Arcene: ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge.