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This is a random sample of the approximately 6 million patient records from GE Medical Quality Improvement Consortium (MQIC) database.
768 Instances of medical information of females of Pima Indian heritage. Originally owned by National Institute of Diabetes and Digestive and Kidney Disease.
Contiene datos sobre pacientes susceptibles de tener diabetes. El campo diabetes contiene la información del diagnóstico. El fichero original disponible en UCI ha sido modificado y extendido con datos ficticios con fines educacionales.
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.
David Gil, dgil '@' dtic.ua.es, Lucentia Research Group, Department of Computer Technology, University of Alicante
Jose Luis Girela, girela '@' ua.es, Department of Biotechnology, University of Alicante
Vehicles involved in car accidents in UK during 2012.
Accidents and vehicles involved
Dataset with results from 4,500 Hospital Patient surveys. The data is from a list of hospital ratings for the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). HCAHPS is a national, standardized survey of hospital patients about their experiences during a recent inpatient hospital stay. https://data.medicare.gov/dataset/Survey-of-Patients-Hospital-Experiences-HCAHPS-/rj76-22dk
Diagnostic with 30 features related to Breast Cancer in Wisconsin. The features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.
The aim is to distinguish between the presence and absence of cardiac arrhythmia and to classify it in one of the 16 groups:
- Class 01 refers to 'normal' ECG
- Classes 02 to 15 refers to different classes of arrhythmia
- Class 16 refers to the rest of unclassified ones.
For the time being, there exists a computer program that makes such a classification. However there are differences between the cardiolog's and the programs classification. Taking the cardiolog's as a gold standard we aim to minimise this difference by means of machine learning tools.