基于XGBoost的肥胖水平综合预测与SHAP模型解释分析  

Comprehensive Prediction of Obesity Level Based on XGBoost and Explanatory Analysis of SHAP Model

作　　者：黄东升 Huang Dongsheng(School of Mathematics and Statistics,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福州大学数学与统计学院,福建福州350108

出　　处：《现代信息科技》2025年第7期40-46,共7页Modern Information Technology

摘　　要：文章旨在利用XGBoost模型预测肥胖水平,通过SHAP方法解释各特征对肥胖风险的贡献,从而识别关键影响因素并提供肥胖预防的科学依据。基于家族肥胖史、饮食习惯、身体活动频率等多个特征进行建模,使用XGBoost预测肥胖水平,并应用SHAP值分析各特征对模型输出的影响,来解释各特征对肥胖分类的贡献。家族肥胖史、年龄和饮食习惯是影响肥胖的关键因素,SHAP分析进一步揭示了这些因素对肥胖分类的具体贡献和影响。通过结合XGBoost的高效预测能力和SHAP的可解释性分析,研究不仅识别了影响肥胖的关键特征,还为个性化健康管理和肥胖预防提供了科学依据,展示了机器学习在公共健康领域的应用潜力。This paper aims to use the XGBoost model to predict obesity levels and explain the contribution of various features to obesity risk through the SHAP method,so as to identify key influencing factors and provide a scientific basis for obesity prevention.Modeling is conducted based on multiple features such as family history of obesity,dietary habits,and frequency of physical activity.XGBoost is used to predict obesity levels,and SHAP values are applied to analyze the impact of each feature on the model output,to explain the contribution of each feature to obesity classification.Family history of obesity,age,and dietary habits are key factors affecting obesity.SHAP analysis further reveals the specific contributions and impact of these factors on obesity classification.By combining the efficient predictive ability of XGBoost and the explanatory analysis of SHAP,this research not only identifies the key features that affect obesity,but also provides a scientific basis for personalized health management and obesity prevention,demonstrating the application potential of Machine Learning in the field of public health.

关 键 词：SHAP XGBoost 大数据 肥胖水平 健康管理 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]