基于XGBoost的肥胖水平综合预测与SHAP模型解释分析

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中图分类号：TP399 文献标识码：A文章编号：2096-4706（2025）07-0040-07

Abstract： This paper aims to use the XGBoost model to predict obesity levels and explain the contribution of various featurestoobesityriskthroughtheSHAPmethd，soastoidentifykeyifuencingfactorsandprovideascientificbasisfor obesity prevention.Modeling isconductedbasedonmultiple featuressuchas familyhistoryofobesity，dietaryhabits，and frequencyofphysicalactivityXGBoostisused topredictobesitylevels，andSHAPvaluesareappliedtoanalyzethempactof eachfeatureonthe modeloutput，toexplainthecontributionofeach feature toobesityclasifcation.Family historyofobesity age，and dietary habitsare keyfactors afectingobesity.SHAPanalysis furtherreveals the specificcontributionsandimpactof thesefactorsonobesityclassification.BycombiningtheeffcientpredictiveabilityofXGBoostandtheexplanatoryanalysisof SHAP，thisresearchnotonlyidentifiesthekeyfeaturesthataffctobesitybutalsoprovidesascientificbasisforpersoalized health management and obesity prevention，demonstrating theapplication potential ofMachine Learming inthe fieldof public health.

Keywords： SHAP; XGBoost; Big Data; obesity level; health management

0 引言

对肥胖水平进行数据挖掘，是指通过分析与肥胖相关的各类数据，揭示肥胖的成因、发展趋势以及与健康风险之间的关系，从而为肥胖的预防、管理和治疗提供数据支持。（剩余8043字）