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

Naheed Ali; Dr. Noor Badshah

Authors

Naheed Ali Dept. of Basic Sciences and Islamiat University of Engineering and Technology Peshawar, Pakistan,
Dr. Noor Badshah Dept. of Basic Sciences and Islamiat University of Engineering and Technology Peshawar, Pakistan,

Keywords:

Artificial Neural Network, Myocardial Infarction, Mathematical Modeling.

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.

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