摘要
Abstract
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Key words
SHAP/XGBoost/Big Data/obesity level/health management分类
信息技术与安全科学
