Comparison of classification results.

Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.体能健康是健康生活的核心要素，超重或缺乏体育锻炼均会引发健康问题。因此，从非医疗、高性价比的视角评估个体的身体健康状态至关重要。 本研究旨在依托全国体检数据评估国民整体身体健康状况，选取12项指标将健康状态划分为优秀、良好、合格与不合格四个等级。 当前研究面临的挑战在于，现有体能健康评估相关文献大多聚焦于体育运动员与在校学生这两类群体，尚未构建起普适性的合理指标体系，且现有评估方法存在局限性，无法推广应用至其他群体。 本研究基于全国体检数据构建了科学合理的健康指标体系，打破群体限制，以全体国民为研究对象，并拟通过机器学习模型为民众提供可辅助自测身体状态的实用健康建议。 本研究通过非参数检验与探索性统计分析，对选取的指标进行了显著性验证；同时采用7种机器学习模型开展对比实验，最终得到适配体能健康测试等级的最优多分类模型。综合实验结果表明，多层感知机（MLP）的分类效果最佳，其宏精确率可达74.4%，微精确率可达72.8%；此外，其召回率均高于70%，且汉明损失最小，仅为0.272。 本研究结果具备显著的实际应用价值：个体可借助该分类模型了解自身的体能健康水平与状态，结合测评指标进行适度锻炼并调整生活方式，这亦是健康管理的重要一环。