A Modified Adam Remora Optimization and Deep Hodgkin–Huxley Neural Network Model for Obesity Risk Assessment in Adolescent Athletes
DOI:
https://doi.org/10.31838/NJAP/08.02.09Keywords:
proficiency barriers, obesity risk, adolescent, sport participants, deep learningAbstract
Adolescent sport participants are young individuals actively engaged in organized sports, encompassing both team and individual activities. The positive impact of physical activity on health and psychological well-being is well-established. However, there is a notable gap in research regarding the influence of physical activity and sports training on a student's knowledge and attitudes about sports. Movement proficiency assessments, including analysis of fundamental movements such as running, jumping, landing, and cutting, play a crucial role in understanding the physical capabilities of adolescent athletes. Concurrently, obesity in adolescents is linked to various health risks, including type 2 diabetes, cardiovascular diseases, and orthopedic issues. Given these risks, the prevention and treatment of obesity in adolescents are paramount. Despite scientific articles acknowledging a decline in obesity rates among adolescents, the factors contributing to this change remain unclear. The purpose of this study is to determine whether movement proficiency and adolescent sport participants and their obesity status. To address this, we design a modified Adam remora optimization (MARO) algorithm for feature analysis, extracting impactful features from the dataset. Furthermore, we present a deep Hodgkin–Huxley neural network (DHH-NN) intended to distinguish hazard of heftiness in teenagers in light of their weight file percentiles and actual wellness levels. To validate the efficacy of our MARO-DHH-NN model, we conduct tests using the benchmark FITescola® project dataset. The results not only validate the performance of our model but also demonstrate its superiority over existing state-of-the-art models.
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