Key result
Differential evolution optimization with acceleration time response yields highest accuracy for detecting myocardial injury.
Why the study?
High-intensity athletic training can cause varying degrees of myocardial damage, motivating the need to evaluate and balance exercise myocardial injury and intensity to actively prevent injury.
Machine learning-based optimization algorithms can accurately and quickly identify myocardial injury in numerical models of high-intensity exercise.
May enhance simulation-based detection of exercise-related injury; leaves open clinical validation before athlete monitoring use.
In order to train at high-intensity, athletics can again cause varying degrees of myocardial damage. Evaluating the balance between exercise myocardial injury and exercise intensity should actively prevent myocardial injury caused by high-intensity athletic training. In this paper, an intelligent optimization algorithm is used to investigate the degree of myocardial injury. The basic idea is to define the measured data and the output of the numerical model as an objective function of the structural parameters, to obtain the structural parameters by finding ways to continuously optimize the objective function to be close to the observed values, and to identify the injury based on the changes in these parameters before and after myocardial injury. The objective function can be defined in various ways, and the myocardial injury optimization algorithm can be chosen. In order to obtain the best computational results, numerical simulations of damage identification are performed using the objective function and three machine learning-based optimization algorithms. The computational results show that the combination of the objective function and the machine learning algorithms provides good accuracy and computational speed in identifying myocardial injury.
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Guanguan Li (2022) studied Myocardial Injury. Machine learning-based optimization algorithms (DE, GA, PSO) vs. Alternative objective functions and algorithms was evaluated on Accuracy and computational speed in identifying myocardial injury. Numerical simulations demonstrated that combining the differential evolution algorithm with an acceleration time response objective function provided the highest accuracy for identifying myocardial injury.
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