Classification confusion matrix.

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.

身体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各类健康问题。因此，从非医疗、高性价比的视角评估个体身体健康状况至关重要。本研究旨在依托国民体质监测数据评估全民身体健康水平，选取12项指标将身体健康状态划分为优秀、良好、合格与不合格四个等级。当前现存的挑战在于，现有体质评估相关研究多聚焦于体育运动员与在校学生这两大群体，且尚未构建起合理的指标体系，现有评估方法存在局限性，无法推广至其他群体。本研究基于国民体质监测数据构建了合理的健康指标体系，打破群体限制，以全民为研究对象，并期望借助机器学习模型为民众提供实用的健康建议，助力其自测身体状态。本研究通过非参数检验与探索性统计分析，验证了所选指标的显著性；采用7种机器学习模型开展实验，最终得到适配体质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）的分类效果最优，其宏精确率可达74.4%，微精确率为72.8%；此外，其召回率均高于70%，且汉明损失（Hamming Loss）最小，仅为0.272。本研究结果具备显著的实际应用价值：民众可通过该分类模型了解自身的体质水平与健康状态，结合评估指标适度锻炼并调整生活方式，这亦是健康管理的重要环节。