1. Abscisic Acid Signaling Network: The objective is to determine the set of boolean rules that describe the interactions of the nodes within this plant signaling network. The dataset includes 300 separate boolean pseudodynamic simulations using an asynchronous update scheme.
2. Audiology (Standardized): Standardized version of the original audiology database
3. Breast Cancer Wisconsin (Original): Original Wisconsin Breast Cancer Database
4. Breast Cancer Wisconsin (Diagnostic): Diagnostic Wisconsin Breast Cancer Database
5. Thoracic Surgery Data: The data is dedicated to classification problem related to the post-operative life expectancy in the lung cancer patients: class 1 - death within one year after surgery, class 2 - survival.
6. Dermatology: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease.
7. Echocardiogram: Data for classifying if patients will survive for at least one year after a heart attack
8. Heart Disease: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach
9. Hepatitis: From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney)
10. Horse Colic: Well documented attributes; 368 instances with 28 attributes (continuous, discrete, and nominal); 30% missing values
11. Lymphography: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. (Restricted access)
12. Forest type mapping: Multi-temporal remote sensing data of a forested area in Japan. The goal is to map different forest types using spectral data.
13. Primary Tumor: From Ljubljana Oncology Institute
14. Soybean (Large): Michalski's famous soybean disease database
15. SPECT Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.
16. SPECTF Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.
17. Zoo: Artificial, 7 classes of animals
18. Cervical cancer (Risk Factors): This dataset focuses on the prediction of indicators/diagnosis of cervical cancer. The features cover demographic information, habits, and historic medical records.
19. Quality Assessment of Digital Colposcopies: This dataset explores the subjective quality assessment of digital colposcopies.
20. Statlog (Heart): This dataset is a heart disease database similar to a database already present in the repository (Heart Disease databases) but in a slightly different form
21. Autistic Spectrum Disorder Screening Data for Children : Children screening data for autism suitable for classification and predictive tasks
22. Autistic Spectrum Disorder Screening Data for Adolescent : Autistic Spectrum Disorder Screening Data for Adolescent. This dataset is related to classification and predictive tasks.
23. HCC Survival: Hepatocellular Carcinoma dataset (HCC dataset) was collected at a University Hospital in Portugal. It contains real clinical data of 165 patients diagnosed with HCC.
24. Parkinsons: Oxford Parkinson's Disease Detection Dataset
25. Breast Tissue: Dataset with electrical impedance measurements of freshly excised tissue samples from the breast.
26. Breast Cancer Coimbra: Clinical features were observed or measured for 64 patients with breast cancer and 52 healthy controls.
27. ILPD (Indian Liver Patient Dataset): This data set contains 10 variables that are age, gender, total Bilirubin, direct Bilirubin, total proteins, albumin, A/G ratio, SGPT, SGOT and Alkphos.
28. Breast Cancer Wisconsin (Prognostic): Prognostic Wisconsin Breast Cancer Database
29. Early biomarkers of Parkinson’s disease based on natural connected speech: Predict a pattern of neurodegeneration in the dataset of speech features obtained from patients with early untreated Parkinsonâ€™s disease and patients at high risk developing Parkinsonâ€™s disease.
30. Fertility: 100 volunteers provide a semen sample analyzed according to the WHO 2010 criteria. Sperm concentration are related to socio-demographic data, environmental factors, health status, and life habits