APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS

Authors

  • Nasir Hamzah Universitas Nusa Mandiri
  • Yan Rianto Universitas Nusa Mandiri

DOI:

https://doi.org/10.33480/pilar.v21i2.6413

Keywords:

accurate diagnosis, Artificial Neural Network Approach, BPNN, early detection, heart attack

Abstract

A heart attack is a medical emergency caused by restricted blood flow to the heart, commonly leading to myocardial infarction due to blood clots or fat accumulation. Early detection of heart disease is crucial to support prevention efforts and assist healthcare professionals in timely diagnosis and treatment. This study applies the Backpropagation Neural Network (BPNN) algorithm as an intelligent computing method for heart attack detection. Experimental results demonstrate a prediction accuracy of 96.47%, confirming the effectiveness of artificial neural networks in identifying heart attacks in patients. These findings highlight the potential of BPNN as a reliable and precise early detection system, which can support more accurate clinical decision-making and improve the effectiveness of heart attack prevention and treatment.

Downloads

Download data is not yet available.

References

Angga Aditya Pratama, Yurika Permanasari, & Didi Suhaedi. (2024). Algoritma Deeplearning menggunakan Backpropagation Neural Network. Bandung Conference Series: Mathematics, 4(1), 21–29. https://doi.org/10.29313/bcsm.v4i1.15299

Devi, A., -, S. D., -, A. K., -, H. K., & -, R. K. (2023). An Exploratory Study on Public Awareness and Action Towards Heart Attack. International Journal For Multidisciplinary Research, 5(3), 3483. https://doi.org/10.36948/ijfmr.2023.v05i03.3483

Dieste-Velasco, M. I. (2021). Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults. Mathematics, 9(24), 3247. https://doi.org/10.3390/math9243247

Emmert‐Streib, F., & Dehmer, M. (2022). Taxonomy of machine learning paradigms: A data‐centric perspective. WIREs Data Mining and Knowledge Discovery, 12(5), e1470. https://doi.org/10.1002/widm.1470

I. Hilaiwah, A. S., Alwally Abed Allah, H. A., Akhudir Abbas, B., & Sutikno, T. (2021). Live to learn: Learning rules-based artificial neural network. Indonesian Journal of Electrical Engineering and Computer Science, 21(1), 558. https://doi.org/10.11591/ijeecs.v21.i1.pp558-565

Joshi, A. P., & Patel, B. V. (2021). Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process. Oriental Journal of Computer Science and Technology, 13(0203), 78–81. https://doi.org/10.13005/ojcst13.0203.03

Kashimpure, R. L. (2023). Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets). International Research Journal of Modernization in Engineering Technology & Science. https://doi.org/10.56726/IRJMETS43049

Li, M. (2024). Comprehensive Review of Backpropagation Neural Networks. Academic Journal of Science and Technology, 9(1), 150–154. https://doi.org/10.54097/51y16r47

M. Mijwil, M. (2021). Artificial Neural Networks Advantages and Disadvantages. Mesopotamian Journal of Big Data, 2021, 29–31. https://doi.org/10.58496/MJBD/2021/006

Mr. Dongare Sadanand & Dr. Sachin Bhosale. (2023). Basic of Artificial Neural Network. International Journal of Advanced Research in Science, Communication and Technology, 299–303. Internet Archive. https://doi.org/10.48175/IJARSCT-8159

Muharram, F. R., Multazam, C. E. C. Z., Mustofa, A., Socha, W., Andrianto, Martini, S., Aminde, L., & Yi-Li, C. (2024). The 30 Years of Shifting in The Indonesian Cardiovascular Burden—Analysis of The Global Burden of Disease Study. Journal of Epidemiology and Global Health, 14(1), 193–212. https://doi.org/10.1007/s44197-024-00187-8

Nokeri, T. C. (2022). Solving Economic Problems Applying Artificial Neural Networks. In T. C. Nokeri, Econometrics and Data Science (pp. 161–188). Apress. https://doi.org/10.1007/978-1-4842-7434-7_8

Qamar, R., & Ali Zardari, B. (2023). Artificial Neural Networks: An Overview. Mesopotamian Journal of Computer Science, 130–139. https://doi.org/10.58496/MJCSC/2023/015

Sharma, O. (2022). Exploring the Statistical Properties and Developing a Non-Linear Activation Function. 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), 1370–1375. https://doi.org/10.1109/ICACRS55517.2022.10029124

Stonier, A. A., Gorantla, R. K., & Manoj, K. (2024). Cardiac disease risk prediction using machine learning algorithms. Healthcare Technology Letters, 11(4), 213–217. https://doi.org/10.1049/htl2.12053

Tabuena, A. C., Hilario, Y. M., & Buenaflor, M. (2021). Overview and Exemplar Components of the Research Methodology on the Research Writing Process for Senior High School Students. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3802722

Veenu. (2021). How training of sigmoidal FFANN affected by weight initialization. In A. Dev, A. Sharma, & S. S. Agarwal, Artificial Intelligence and Speech Technology (1st ed., pp. 37–43). CRC Press. https://doi.org/10.1201/9781003150664-5

Downloads

Published

2025-09-23

How to Cite

Hamzah, N., & Rianto, Y. (2025). APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS. Jurnal Pilar Nusa Mandiri, 21(2), 198–207. https://doi.org/10.33480/pilar.v21i2.6413

Issue

Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for September 2025

Section

Articles

License

Copyright (c) 2025 Nasir Hamzah, Yan Rianto

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:

  1. Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
  2. Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
  3. Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
    Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.