Data-augmented machine learning for personalized carbohydrate-protein supplement recommendation for endurance.

ABSTRACT: Carbohydrate-protein supplementation often improves endurance performance. However, effectiveness varies significantly among individuals due to unique personal characteristics. This study aimed to develop a predictive machine learning framework for personalized supplementation, with a core methodological novelty in applying a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to address the critical issue of data scarcity. Based on 231 rowing trials, the framework utilized 46 input features covering baseline characteristics and dietary intakes. Rowing distance was the performance outcome. The machine learning pipeline first utilized a hybrid feature selection method (correlation analysis, model-based importance, and domain knowledge). Following a comparative evaluat

SUBMITTER: Xiangyu W 

PROVIDER: S-EPMC12624012 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature