Document Type
Article
Publication Date
5-1-2015
Abstract
In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords
Machine learning, data classification, medical signals, electrocardiogram, auscultatory blood pressure, support vector machines, feature-selection algorithm, neural-network model, arrhythmia classification, ecg arrhythmia, comparing performances, logistic-regression, biomedical signals, wavelet transform, decision tree.
Divisions
fac_eng
Funders
University of Malaya UM.C/HIR/MOHE/ENG/50
Publication Title
Expert Systems with Applications
Volume
42
Issue
7
Publisher
PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
Additional Information
Cc2nt Times Cited:0 Cited References Count:67