Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field...

Area of Science:

• Sports Medicine
• Biomedical Engineering
• Computational Biology

Background:

• High-intensity training in athletics poses a risk of myocardial injury.
• Evaluating the relationship between exercise intensity and cardiac damage is crucial for prevention.

Purpose of the Study:

• To investigate and identify the degree of myocardial injury resulting from high-intensity athletic training.
• To develop an intelligent optimization approach for assessing exercise-induced cardiac damage.

Main Methods:

• Utilized an intelligent optimization algorithm to define measured data and numerical model outputs as an objective function.
• Optimized structural parameters to align objective function with observed values, identifying injury based on parameter changes.
• Performed numerical simulations using the objective function and three machine learning-based optimization algorithms for damage identification.

Main Results:

• The combination of the objective function and machine learning algorithms demonstrated high accuracy in identifying myocardial injury.
• The proposed methods achieved good computational speed in detecting exercise-induced cardiac damage.
• Successfully identified myocardial injury by analyzing changes in structural parameters before and after high-intensity training.

Conclusions:

• Intelligent optimization algorithms, particularly when combined with machine learning, offer a robust approach for detecting myocardial injury in athletes.
• This methodology aids in balancing exercise intensity with cardiac health, enabling proactive prevention of training-related myocardial damage.