A machine learning framework for personalized exercise prescription based on BMI and physical fitness assessment.

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. 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-up test(muscular strength), sit-up test (muscular endurance), and 30 × 2 shuttle run (anaerobic capacity). 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 test 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. It’s well-suited for both clinical and mobile healthcare applications, addressing the growing demand for personalized exercise interventions among young adults.