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Modeling of Post-Myocardial Infarction and Its Solution Through Artificial Neural Network

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  • Naheed Ali, Dr. Noor Badshah

    (Dept. of BasicSciences and IslamiatUniversity of Engineering and Technology Peshawar, Pakistan)

Abstract

Cardiovascular diseases, particularly myocardial infarction (MI) constitute a significant health concern globally. A myocardial infarction, which is commonly known as a heart attack, happens when a part of the heart muscle doesn’t get enough blood because of a blockage. Studying MI is complex and it requires looking at it from different angles. In recent years the fusion of mathematical modeling and artificial intelligence (AI) techniques has emerged as a promising avenue for understanding the complexities associated with MI. The primary goal of this study is to provide an AI-based solution for a new nonlinear mathematical model related to myocardial infarction phenomena. To obtain the solution we will use a well-known deep learning technique, known as artificial neural networks (ANNs) with the combination of the optimization technique Levenberg-Marquardt back propagation (LMB). This combined method is referred to as ANNs-LMB. The results obtained from the model using ANNs-LMB are compared with a reference dataset constructed through the adaptive MATLAB solver ode45. The numerical performance is validated through a reduction in mean square error (MSE). The MSE is around and the obtained results by ANNs-LMB almost overlapped with the reference dataset, which shows the accuracy and efficiency of the proposed methodology.

Suggested Citation

  • Naheed Ali, Dr. Noor Badshah, 2024. "Modeling of Post-Myocardial Infarction and Its Solution Through Artificial Neural Network," International Journal of Innovations in Science & Technology, 50sea, vol. 6(5), pages 18-29, May.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:5:p:18-29
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    References listed on IDEAS

    as
    1. P. Sáez & E. Kuhl, 2016. "Computational modeling of acute myocardial infarction," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 19(10), pages 1107-1115, July.
    2. Lo Schiavo, Mauro & Prinari, Barbara & Gronski, Jessica A. & Serio, Angelo V., 2015. "An artificial neural network approach for modeling the ward atmosphere in a medical unit," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 116(C), pages 44-58.
    3. Muhammad Umar & Zulqurnain Sabir & Muhammad Asif Zahoor Raja & Shumaila Javeed & Hijaz Ahmad & Sayed K. Elagen & Ahmed Khames, 2021. "Numerical Investigations through ANNs for Solving COVID-19 Model," IJERPH, MDPI, vol. 18(22), pages 1-15, November.
    4. Ying Wen & Temuer Chaolu & Xiangsheng Wang, 2022. "Solving the initial value problem of ordinary differential equations by Lie group based neural network method," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-20, April.
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