Arrhythmia
Abstract
Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.
Purpose
Additional Information This database contains 279 attributes, 206 of which are linear valued and the rest are nominal. Concerning the study of H. Altay Guvenir: "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 and 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." The names and id numbers of the patients were recently removed from the database.
| Name | Role | Type | Description | Missing |
|---|---|---|---|---|
| Variable 1 | Feature | Integer | - | No |
| Variable 2 | Feature | Integer | - | No |
| Variable 3 | Feature | Integer | - | No |
| Variable 4 | Feature | Integer | - | No |
| Variable 5 | Feature | Integer | - | No |
| Variable 6 | Feature | Integer | - | No |
| Variable 7 | Feature | Integer | - | No |
| Variable 8 | Feature | Integer | - | No |
| Variable 9 | Feature | Integer | - | No |
| Variable 10 | Feature | Integer | - | No |
Papers Citing this Dataset
19 papers found
Time Warp Invariant Dictionary Learning for Time Series Clustering: Application to Music Data Stream Analysis
By Saeed Yazdi, Ahlame Chouakria, Patrick Gallinari, Manuel Moussallam.
Adversarially Learned Anomaly Detection
By Houssam Zenati, Manon Romain, Chuan Foo, Bruno Lecouat, Vijay Chandrasekhar.
Attribute Reduction-Based Ensemble Rule Classifiers Method for Dataset Classification
By Mohammad Basir, Faudziah Ahmad.
Nonconvex Sparse Logistic Regression with Weakly Convex Regularization
By Xinyue Shen, Yuantao Gu.
Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection
By Mohammad Dowlatshahi, Vali Derhami, Hossein Nezamabadi-pour.