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

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

Medical charges in the USA. Average total payment based on state demographic data and 2012 elections winner in the state.

Source

• Demographics from Esri at Azure Datamarket

• Medical data from cms.gov

Medical charges in the USA. Average total payment based on state demographic data.

Source

• Demographics from Esri at Azure Datamarket

• Medical data from cms.gov

Cause of death in USA prediction based in deaths in the age ranges

Car accidents in UK during 2012.

Source

data.gov.uk

Medical charges in the USA. Average total payment based on state demographic data and 2012 elections winner in the state.

Source

• Demographics from Esri at Azure Datamarket

• Medical data from cms.gov

Urine Analysis Data

Source

vincentarelbundock datasets

The records represent individual data including first and family name, sex, date of birth and postal code, which were collected through iterative insertions in the course of several years. The comparison patterns in this data set are based on a sample of 100.000 records dating from 2005 to 2008. Data pairs were classified as "match" or "non-match" during an extensive manual review where several documentarists were involved.

The resulting classification formed the basis for assessing the quality of the registry’s own record linkage procedure.

In order to limit the amount of patterns a blocking procedure was applied, which selects only record pairs that meet specific agreement conditions. The results of the following six blocking iterations were merged together:

- Phonetic equality of first name and family name, equality of date of birth.

Phonetic equality of first name, equality of day of birth. - Phonetic equality of first name, equality of month of birth.

Phonetic equality of first name, equality of year of birth. - Equality of complete date of birth.

Phonetic equality of family name, equality of sex.

This procedure resulted in 5.749.132 record pairs, of which 20.931 are matches.

The data set is split into 10 blocks of (approximately) equal size and ratio of matches to non-matches.

Record Linkage Comparison Patterns Data Set at UCI