Patient satisfaction survey.

Data courtesy of http://www.hcahpsonline.org. Centers for Medicare & Medicaid Services, Baltimore, MD.

Dataset on thyroid disease, demographic data and the outcomes of several medical tests.

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.

Source

Arrhythmia Data Set at UCI Machine Learning Repository

Model predicting hospital readmissions, by Major Diagnostic Category (MDC) and age.

Data sourced from HCUPnet - a services of the US Department of Health & Human Services: http://hcupnet.ahrq.gov/HCUPnet.jsp.

predicting CHD in SA

768 Instances of medical information of females of Pima Indian heritage. Originally owned by National Institute of Diabetes and Digestive and Kidney Disease.

This model predicts one of three spinal statuses: normal, Hernia or Spondylolisthesis, based on a number of metrics.

The source is taken from UCI Machine learning Repository, from the Vertebral Column Dataset

Model that predicts which percentage of patients will rate a hospital at 9 or 10, based on a dataset of hospital patient surveys. It shows which survey items and which scores are important for a good overall result. 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

This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.)

This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.

Source

Breast Cancer Data Set at UCI Repository