Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2372
Title: Feature Extraction Methods for Predicting the Prevalence of Heart Disease
Authors: Ngong, I.C.
Baykan, Nurdan
Keywords: Arrhythmias
Boosted Trees (BT)
Classification
ECG
Feature extraction
Heart disease
Principal Component Analysis (PCA) and Convolutional Neural Networks (CNN)
Random Forest (RF)
Support Vector Machines (SVM)
Issue Date: 2022
Publisher: Springer Science and Business Media Deutschland GmbH
Abstract: This paper presents an automatic classification technique for the detection of cardiac arrhythmias from ECG signals. With cardiac arrhythmias being one of the leading causes of death in the world, accurate and early detection of beat abnormalities can significantly reduce mortality rates. ECG signals are vastly used by physicians for diagnosing heart problems and abnormalities as a result of its simplicity and non-invasive nature. The aim of this study is to determine the most accurate combination of feature extraction methods and SVM (Support Vector Machine) kernel classifier that will produce the best results on ECG signals obtained from the MIT-BIH Arrhythmia Database. SVM classifiers with four different kernels (linear, polynomial, radial basis, and sigmoid) were used to classify different features extracted from the four feature selection methods; Random Forests, XGBoost, Principal Component Analysis, and Convolutional Neural Networks. The CNN-SVM classifier produced the best results overall, with the polynomial kernel achieving the maximum accuracy of 99.2%, the best sensitivity 92.40% from the radial basis kernel, and best specificity of 98.92% from the linear kernel. The high classification accuracy obtained is comparable to or even better than other approaches in literature. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Description: 6th International Conference on Smart City Applications, SCA 2021 -- 27 October 2021 through 29 October 2021 -- -- 274389
URI: https://doi.org/10.1007/978-3-030-94191-8_39
https://hdl.handle.net/20.500.13091/2372
ISBN: 9783030941901
ISSN: 2367-3370
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

Show full item record

CORE Recommender

Page view(s)

32
checked on Feb 6, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.