Research on Prediction and Application of Physical Fitness Test Data Using Ensemble Learning Models

This study proposes an ensemble-learning-based prediction framework for physical fitness assessment using physiological and performance data acquired from conventional testing systems and wearable sensing platforms. Student attributes, including gender, height, weight, and vital capacity, are utilized as input features, while physical fitness indicators are treated as prediction targets. To improve prediction robustness and anomaly sensitivity, a two-stage ensemble architecture combining decision trees and multilayer perceptrons is developed. The decision tree provides preliminary predictions and suppresses the influence of outliers, whereas the multilayer perceptron performs nonlinear feature refinement to enhance predictive accuracy. Experimental evaluation based on 3,000 university students demonstrates that the proposed framework consistently achieves lower prediction errors than individual learning models in both 50-meter sprint and standing long jump assessments. Furthermore, anomaly analysis enables the identification of potential measurement deviations and performance-improvement opportunities. Longitudinal validation indicates that students selected through the proposed framework and subjected to personalized interventions achieve significantly greater improvements than control groups. By interpreting physical fitness assessment as a wearable sensing and data-analysis problem, the proposed approach establishes a closed-loop process linking data acquisition, anomaly detection, performance prediction, and intervention optimization. The framework provides an engineering-oriented methodology for intelligent health monitoring, signal-based performance evaluation, and personalized training support in large-scale monitoring environments.