This is a random sample of the approximately 6 million patient records from GE Medical Quality Improvement Consortium (MQIC) database.

Source:

• http://www.visualizing.org/datasets/mqic-patient-data-100k-sample

In the U.K 2.6 million people have diabetes.It can have serious health consequences if not diagnosed immediately. A new improved way of detecting Diabetes which can change lives with this model. Data UCI

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