A Differential Evolutionary-Based XGBoost for Solving Classification of Physical Fitness Test Data of College Students

The physical health of college students is an important basis for societal development, which directly impacts the competitiveness of future talents and the overall vitality of the nation. To accurately and timely identify the physical health status of college students, a hybrid model of DE-XGBoost is proposed in this study: a discrete coding strategy is designed to solve the XGBoost hyperparameter optimization problem, and differential evolution (DE) is used to achieve global parameter optimization. Based on 20,452 physical test records of a university in 2022, the empirical comparison shows that the accuracy rate, recall rate, and F1 value of the model are improved by 3.5–7.9% compared with support vector machine (SVM), gradient boosting machine (GBM), and multi-layer perceptron (MLP), showing significant performance advantages. This research provides a novel and efficient framework for physical fitness classification, with potential applications in educational curriculum design.