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Arrhythmia
Multivariate Classification Available

Arrhythmia

Donated Dec 31, 1997 Computer Science Creative Commons Attribution 4.0 International

Abstract

Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.

Instances
452
Features
279
Data Type
Multivariate
Missing Values
Yes

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

Arrhythmia.data

CSV N/A • Data

arrhythmia.names

MD N/A • Documentation

Papers Citing this Dataset

19 papers found

Improved linear classifier model with Nyström

By Changming Zhu, Xiang Ji, Chao Chen, Rigui Zhou, Lai Wei, Xiafen Zhang.

PloS one. 2018 88

A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification

By Ke Ren, Haichuan Yang, Yu Zhao, Mingshan Xue, Hongyu Miao, Shuai Huang, Ji Liu.

ArXiv 2018 88

Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element

By Francisco Pérez, José Berná-Martínez, Alberto Oliva, Miguel Ortega.

Complexity. 2018 88

A comprehensive empirical comparison of hubness reduction in high-dimensional spaces

By Roman Feldbauer, Arthur Flexer.

Knowledge and Information Systems. 2018 88

A Fuzzy-Rough based Binary Shuffled Frog Leaping Algorithm for Feature Selection

By Javad Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Ahn.

ArXiv 2018 88
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