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Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Maie Aboghazalah1,*, Passent El-kafrawy2, Abdelmoty M. Ahmed3, Rasha Elnemr5, Belgacem Bouallegue3, Ayman El-sayed4

1 Math and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-kom, Egypt
2 College of Engineering, Computer Science Department, Effat University, Jeddah, Kingdom of Saudi Arabia
3 Department of Computer Engineering, College of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia
4 Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Shebin El-kom, Egypt
5 Climate Change Information Center and Expert Systems, Agriculture Research Center, Giza, Egypt

* Corresponding Author: Maie Aboghazalah. Email: email

Computers, Materials & Continua 2024, 79(3), 3855-3875. https://doi.org/10.32604/cmc.2023.039936

Abstract

Heart monitoring improves life quality. Electrocardiograms (ECGs or EKGs) detect heart irregularities. Machine learning algorithms can create a few ECG diagnosis processing methods. The first method uses raw ECG and time-series data. The second method classifies the ECG by patient experience. The third technique translates ECG impulses into Q waves, R waves and S waves (QRS) features using richer information. Because ECG signals vary naturally between humans and activities, we will combine the three feature selection methods to improve classification accuracy and diagnosis. Classifications using all three approaches have not been examined till now. Several researchers found that Machine Learning (ML) techniques can improve ECG classification. This study will compare popular machine learning techniques to evaluate ECG features. Four algorithms—Support Vector Machine (SVM), Decision Tree, Naive Bayes, and Neural Network—compare categorization results. SVM plus prior knowledge has the highest accuracy (99%) of the four ML methods. QRS characteristics failed to identify signals without chaos theory. With 99.8% classification accuracy, the Decision Tree technique outperformed all previous experiments.

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APA Style
Aboghazalah, M., El-kafrawy, P., Ahmed, A.M., Elnemr, R., Bouallegue, B. et al. (2024). Arrhythmia detection by using chaos theory with machine learning algorithms. Computers, Materials & Continua, 79(3), 3855-3875. https://doi.org/10.32604/cmc.2023.039936
Vancouver Style
Aboghazalah M, El-kafrawy P, Ahmed AM, Elnemr R, Bouallegue B, El-sayed A. Arrhythmia detection by using chaos theory with machine learning algorithms. Comput Mater Contin. 2024;79(3):3855-3875 https://doi.org/10.32604/cmc.2023.039936
IEEE Style
M. Aboghazalah, P. El-kafrawy, A.M. Ahmed, R. Elnemr, B. Bouallegue, and A. El-sayed "Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms," Comput. Mater. Contin., vol. 79, no. 3, pp. 3855-3875. 2024. https://doi.org/10.32604/cmc.2023.039936



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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