IDENTIFIKASI KELAINAN JANTUNG DARI DATA EKG MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK
DOI:
https://doi.org/10.47080/atzc0r27Keywords:
accuracy, backpropagation, electrocardiogram, heart abnormalities, mean squared error (MSE)Abstract
This study is one of the initial approaches in implementing Backpropagation Neural Network for ECG signal classification. The condition of the human heart can be known based on the results of electrocardiogram medical records, so that with the results of electrocardiogram medical records it can be known whether the heart is normal or abnormal. Symptoms of abnormal heart disease in the heart often come suddenly. Early recognition of heart disease with further procedures and treatment can prevent an increase in the risk of fatal heart attacks. This study has a very important goal in an effort to detect and classify heart abnormalities more efficiently. By utilizing artificial neural networks (ANN) and backpropagation methods, it can utilize computing capabilities to analyze patterns in electrocardiogram (ECG) data. The results show that the classification of heart abnormalities with an epoch value of 2000, a learning rate of 0.01 with normal and abnormal targets, obtained the number of Hidden Neurons as many as 25, the number of weight patterns 44 and a mean squared error (MSE) value of with an accuracy of 0.61364 from 25 inputs.
References
Amutha, S., & Sekar, J. R. (2023).Deep neural network based cardiovascular disease prediction using binarized butterfly optimization. Intelligent Automation & Soft Computing, 36(2), 1863–1880. DOI: 10.32604/iasc.2023.028903
Alromema, W., Alduweib, E., & Abduh, Z. (2023).Heart Sound Classification Using the Nonlinear Dynamic Feature Approach Along with Conventional Classifiers. Engineering, Technology & Applied Science Research, 13(3), 10808–10813.DOI: 10.48084/etasr.5873
El-Ibrahimi, A., Terrada, O., El Gannour, O., Cherradi, B., El Abbassi, A., & Bouattane, O. (2023).Optimizing machine learning algorithms for heart disease classification and prediction. International Journal of Online and Biomedical Engineering (iJOE), 19(15), 61–76. DOI: 10.3991/ijoe.v19i15.42653
García-Ordás, M. T., Bayón-Gutiérrez, M., Benavides, C., et al. (2023).Heart disease risk prediction using deep learning techniques with feature augmentation. Multimedia Tools and Applications, 82, 31759– 31773. DOI: 10.1007/s11042-023-14817-z
Keikhosrokiani, P., Naidu A/P Anathan, A. B., Fadilah, S. I., Manickam, S., & Li, Z. (2023). eartbeat Sound Classification Using a Hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Bee Colony. Digital Health, 9, 20552076221150741. DOI: 10.1177/20552076221150741
Omankwu, O. C., & Ubah, V. I. (2023).Hybrid deep learning model for heart disease prediction using recurrent neural network (RNN). NIPES - Journal of Science and Technology Research, 5(2). DOI: 10.5281/zenodo.8014330
Paul, B., & Karn, B. (2023). Heart disease prediction using scaled conjugate gradient backpropagation of artificial neural network. Soft Computing, 27, 6687–6702. DOI: 10.1007/s00500-022-07649-w.
Ramadani, R., & Budianita, E. (2022).Klasifikasi penyakit jantung koroner menggunakan metode backpropagation neural network (Studi kasus: Rumah Sakit Ibnu Sina Pekanbaru). Seminar Nasional Teknologi Informasi Komunikasi dan Industri
Rapaka, A., Navuri, N. K., Vobbilineni, T., Ibrahim, S. H. S., Deepthi, G. B., & Kocherla, R. (2025). An Improved CNN-Transformer Hybrid Architecture for Heart Sound Classification. South Eastern European Journal of Public Health, 25(1), 2744–2762. DOI: 10.70135/seejph.vi.3190
Sumiati, Hendriyati, P., Triayudi, A., Yusta, A., & Syaefudin, A. (2024).Classification of heart disorders using an artificial neural network approach. Journal of Internet Services and Information Security, 14(2).DOI: 10.58346/JISIS.2024.I2.012E-Journal UIN Suska
S.Radhimeenakshi, G.M.Nasira”Prediction of Heart Diease using Nerual Network with Back Propagation” Pages:1166-1169.2021
Shuvo, S. B., Alam, S. S., Ayman, S. U., Chakma, A., Barua, P. D., & Acharya, U. R. (2023).NRC-Net: Automated Noise Robust Cardio Net for Detecting Valvular Cardiac Diseases Using Optimum Transformation Method with Heart Sound Signals. arXiv preprint arXiv:2305.00141.
Sumiati., Saragih, Hago., Rahman, T. K. A., Triayudi, Agung. (2021). Certainty Cognitive Map (CCM) for Assessing Cognitive Map Causality Using Certainty Factors for Cardiac Failure ICIC Express Letters – An International Journal of Research and Surveys, Vol. 15 (No. 1).
