Klasifikasi MIT-BIH Arrhythmia Database Metode Random Forest dan CNN dengan Model ResNet-50: A Systematic Literature Review

  • M. Rizky ULBI (Universitas Logistik & Bisnis Internasional)
  • Roni Andarsyah ULBI (Universitas Logistik & Bisnis Internasional)
Keywords: Machine Learning, Extraction, ECG, Classification


Although Machine Learning and Deep Learning technologies have been widely used and have shown high accuracy in many applications, including in the health field, their application in early detection of heart disease still has room for improvement. Further research is needed to enhance the accuracy and efficiency of this process. This study aims to understand and improve the process of ECG signal extraction and classification based on Machine Learning and Deep Learning. Essentially, this research aims to evaluate and compare various models, focusing on the Random Forest and Convolutional Neural Networks (CNN) models. The study reviews several related researches, especially those focusing on the process of extraction and classification of ECG signals using Machine Learning and Deep Learning. After extraction and classification of data, an evaluation and comparison process is conducted to determine the best performing model. From the research conducted, it was found that Machine Learning methods generally show an accuracy rate between 97.02% - 99.66%, with the Random Forest method having an accuracy of 97.02%. Meanwhile, the CNN method shows a higher accuracy rate, which is between 98.75% - 100%. Thus, this research confirms the superiority of CNN in this classification process, and shows potential for further use in early detection of heart disease.


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D. O. Eke, “Chatgpt and the rise of Generative AI: Threat to academic integrity?,” Journal of Responsible Technology, vol. 13, p. 100060, 2023. doi:10.1016/j.jrt.2023.100060

Pojon, M. (2017). Using machine learning to predict student performance (Master's thesis).

I. M. Agus Oka Gunawan, I. D. Indah Saraswati, I. D. Riswana Agung, and I. P. Eka Putra, “Klasifikasi Penyakit jantung menggunakan algoritma decision Tree Series C4.5 Dengan Rapidminer,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 5, no. 2, pp. 73–83, 2023. doi:10.47233/jteksis.v5i2.775

J. Siswanto, A. A. Qalban, and S. N. Lahay, “Aplikasi Sistem Pakar Klasifikasi Kesehatan Lingkungan Permukiman Dengan metode certainty factors,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 5, no. 2, pp. 103–112, 2023. doi:10.47233/jteksis.v5i2.787

R. Ganeshkumar and Dr. YSKumaraswamy, “Investigating cardiac arrhythmia in ECG using Random Forest Classification,” International Journal of Computer Applications, vol. 37, no. 4, pp. 31–34, 2012. doi:10.5120/4599-6557

J. J. Kim, H. Jang, and S. Roh, “A systematic literature review on humanitarian logistics using network analysis and Topic modeling,” The Asian Journal of Shipping and Logistics, vol. 38, no. 4, pp. 263–278, 2022. doi:10.1016/j.ajsl.2022.10.003

H. Almutairi, G. M. Hassan, and A. Datta, “Classification of obstructive sleep apnoea from single-lead ECG signals using convolutional neural and long short term memory networks,” Biomedical Signal Processing and Control, vol. 69, p. 102906, 2021. doi:10.1016/j.bspc.2021.102906

P. Kanani and M. Padole, “ECG Heartbeat arrhythmia classification using time-series augmented signals and Deep Learning Approach,” Procedia Computer Science, vol. 171, pp. 524–531, 2020. doi:10.1016/j.procs.2020.04.056

D. K. Atal and M. Singh, “Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network,” Computer Methods and Programs in Biomedicine, vol. 196, p. 105607, 2020. doi:10.1016/j.cmpb.2020.105607

S. Mandal, P. Mondal, and A. H. Roy, “Detection of ventricular arrhythmia by using heart rate variability signal and ECG Beat Image,” Biomedical Signal Processing and Control, vol. 68, p. 102692, 2021. doi:10.1016/j.bspc.2021.102692

R. K. Tripathy, M. R. A. Paternina, J. G. Arrieta, A. Zamora-Méndez, and G. R. Naik, “Automated detection of congestive heart failure from Electrocardiogram Signal using Stockwell transform and hybrid classification scheme,” Computer Methods and Programs in Biomedicine, vol. 173, pp. 53–65, 2019. doi:10.1016/j.cmpb.2019.03.008

J. Rahul, M. Sora, L. D. Sharma, and V. K. Bohat, “An improved cardiac arrhythmia classification using an RR interval-based approach,” Biocybernetics and Biomedical Engineering, vol. 41, no. 2, pp. 656–666, 2021. doi:10.1016/j.bbe.2021.04.004

S. Sowmya and D. Jose, “Contemplate on ECG signals and classification of arrhythmia signals using CNN-LSTM deep learning model,” Measurement: Sensors, vol. 24, p. 100558, 2022. doi:10.1016/j.measen.2022.100558

Y.-Y. Jo et al., “Detection and classification of arrhythmia using an explainable deep learning model,” Journal of Electrocardiology, vol. 67, pp. 124–132, 2021. doi:10.1016/j.jelectrocard.2021.06.006

L. Zheng, Z. Wang, J. Liang, S. Luo, and S. Tian, “Effective compression and classification of ECG arrhythmia by Singular Value Decomposition,” Biomedical Engineering Advances, vol. 2, p. 100013, 2021. doi:10.1016/j.bea.2021.100013

S. C. Mohonta, M. A. Motin, and D. K. Kumar, “Electrocardiogram based arrhythmia classification using wavelet transform with Deep Learning Model,” SSRN Electronic Journal, 2022. doi:10.2139/ssrn.4088025

K. Prashant, P. Choudhary, T. Agrawal, and E. Kaushik, “Owae-net: COVID-19 detection from ECG images using deep learning and optimized weighted average ensemble technique,” Intelligent Systems with Applications, vol. 16, p. 200154, 2022. doi:10.1016/j.iswa.2022.200154

V. Jahmunah, E. Y. K. Ng, T. R. San, and U. R. Acharya, “Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GABORCNN model with ECG signals,” Computers in Biology and Medicine, vol. 134, p. 104457, 2021. doi:10.1016/j.compbiomed.2021.104457

A. S. Eltrass, M. B. Tayel, and A. I. Ammar, “A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-q non-stationary Gabor transform,” Biomedical Signal Processing and Control, vol. 65, p. 102326, 2021. doi:10.1016/j.bspc.2020.102326

A. A. Bhurane, M. Sharma, R. San-Tan, and U. R. Acharya, “An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG Signals,” Cognitive Systems Research, vol. 55, pp. 82–94, 2019. doi:10.1016/j.cogsys.2018.12.017

M. Porumb, E. Iadanza, S. Massaro, and L. Pecchia, “A convolutional neural network approach to detect congestive heart failure,” Biomedical Signal Processing and Control, vol. 55, p. 101597, 2020. doi:10.1016/j.bspc.2019.101597

A. Rath, D. Mishra, G. Panda, and S. C. Satapathy, “Heart disease detection using deep learning methods from imbalanced ECG samples,” Biomedical Signal Processing and Control, vol. 68, p. 102820, 2021. doi:10.1016/j.bspc.2021.102820

A. Pal, R. Srivastva, and Y. N. Singh, “CardioNet: An efficient ECG arrhythmia classification system using transfer learning,” Big Data Research, vol. 26, p. 100271, 2021. doi:10.1016/j.bdr.2021.100271

S. Kuila, N. Dhanda, and S. Joardar, “Feature extraction and classification of MIT-BiH Arrhythmia Database,” Lecture Notes in Electrical Engineering, pp. 417–427, 2019. doi:10.1007/978-981-15-0829-5_41

Z. F. Apandi, R. Ikeura, and S. Hayakawa, “Arrhythmia detection using MIT-BiH Dataset: A Review,” 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), 2018. doi:10.1109/icassda.2018.8477620

T. Li and M. Zhou, “ECG classification using wavelet packet entropy and random forests,” Entropy, vol. 18, no. 8, p. 285, 2016. doi:10.3390/e18080285

R. Ganeshkumar and Dr. YSKumaraswamy, “Investigating cardiac arrhythmia in ECG using Random Forest Classification,” International Journal of Computer Applications, vol. 37, no. 4, pp. 31–34, 2012. doi:10.5120/4599-6557

M. Wasimuddin, K. Elleithy, A.-S. Abuzneid, M. Faezipour, and O. Abuzaghleh, “Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey,” IEEE Access, vol. 8, pp. 177782–177803, 2020. doi:10.1109/access.2020.3026968

N. K. Kamila et al., “Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9991–10009, 2022. doi:10.1016/j.jksuci.2022.10.001

C. Renggli, S. Ashkboos, M. Aghagolzadeh, D. Alistarh, and T. Hoefler, “SparCML,” Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2019. doi:10.1145/3295500.3356222

H. Bhatt, V. Shah, K. Shah, R. Shah, and M. Shah, “State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: A comprehensive review,” Intelligent Medicine, 2022. doi:10.1016/j.imed.2022.08.004

M. Khanzadeh, S. Chowdhury, M. Marufuzzaman, M. A. Tschopp, and L. Bian, “Porosity prediction: Supervised-learning of thermal history for direct laser deposition,” Journal of Manufacturing Systems, vol. 47, pp. 69–82, 2018. doi:10.1016/j.jmsy.2018.04.001

N. Jalal, A. Mehmood, G. S. Choi, and I. Ashraf, “A novel improved random forest for text classification using feature ranking and optimal number of trees,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 2733–2742, 2022. doi:10.1016/j.jksuci.2022.03.012

D. Makariou, P. Barrieu, and Y. Chen, “A random forest based approach for predicting spreads in the primary catastrophe bond market,” Insurance: Mathematics and Economics, vol. 101, pp. 140–162, 2021. doi:10.1016/j.insmatheco.2021.07.003

M. Brendel et al., “Application of deep learning on single-cell RNA sequencing data analysis: A Review,” Genomics, Proteomics & Bioinformatics, vol. 20, no. 5, pp. 814–835, 2022. doi:10.1016/j.gpb.2022.11.011

H. Naeem and A. A. Bin-Salem, “A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images,” Applied Soft Computing, vol. 113, p. 107918, 2021. doi:10.1016/j.asoc.2021.107918

How to Cite
Rizky, M., & Andarsyah, R. (2023). Klasifikasi MIT-BIH Arrhythmia Database Metode Random Forest dan CNN dengan Model ResNet-50: A Systematic Literature Review. Jurnal Teknologi Dan Sistem Informasi Bisnis, 5(3), 190-196. https://doi.org/10.47233/jteksis.v5i3.825