A Machine Learning Framework for Personalized Exercise Prescription Based on BMI and Physical Fitness Assessment

BACKGROUND: This study proposes a hybrid machine learning framework that integrates one-dimensional convolutional neural networks (1D-CNN) with multi-head attention and Light Gradient Boosting Machines (LightGBM) to model the relationship between physical fitness and body mass index (BMI), thereby generating personalized exercise prescriptions. METHODS: The dataset consists of 6,698 male students aged 18–20 years, including BMI measurements alongside four standardized fitness indicators: 3,000-meter run (aerobic capacity), pull-ups (muscular strength), sit-ups (muscular endurance), and shuttle run (anaerobic capacity). RESULTS: The 1D-CNN + Attention module effectively captures both local and global temporal patterns, while LightGBM significantly enhances classification accuracy through gradient-boosted decision trees. The proposed hybrid architecture achieved state-of-the-art performance in BMI classification, with an accuracy of 94.5% (Cohen’s κ  = 0.91) and an F1 score of 0.93, outperforming traditional classifiers by 12.3% to 19.1%. Model interpretability is ensured through SHapley Additive exPlanations (SHAP), which supports dynamic prescription adjustments aimed at improving muscular strength, cardiorespiratory endurance, speed, agility, and flexibility. A 12-week randomized trial demonstrated the clinical efficacy of this framework, yielding a 23.5% reduction in overweight and obesity prevalence, a 15.2% increase in pull-up performance, and a 9.8% improvement in 30×2 shuttle run results. With an inference time of less than 0.8 milliseconds per sample and robust clinical outcomes, this framework provides a scalable real-time solution for data-driven health optimization. CONCLUSIONS: It’s well-suited for both clinical and mobile healthcare applications, addressing the growing demand for personalized exercise interventions among young adults.