Assessment of ensemble learning application in predicting body fat percentage in adolescents
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1282Keywords:
Ensemble Learning, Body Fat, Nutritional TransitionAbstract
Objective: The present study aimed to estimate the percentage of body fat in adolescents from São Luís/MA using machine learning techniques. Method: Ensemble techniques with the algorithms Stacking, Bagging, and AdaBoost were employed. Results: The findings revealed that the Stacking model demonstrated the best performance, with lower mean squared error (MSE) and higher coefficient of determination (R²), indicating its effectiveness in explaining the data variability. Conclusion: Stacking is the most suitable algorithm for predicting body fat index in adolescents as it adapted well to the data due to its robustness, reduction of overfitting, and high interpretative power.
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