机器学习预测重度吸烟者卒中风险—NHANES研究  

Machine learning for predicting stroke risk in heavy smokers-NHANES Study

作　　者：施学松 刘亚 SHI Xuesong;LIU Ya(Department of Neurology,Xuyi County People's Hospital,Huai'an 211700,Jiangsu Province,China)

机构地区：[1]盱眙县人民医院神经内科,江苏盱眙211700

出　　处：《中国数字医学》2025年第3期26-35,共10页China Digital Medicine

摘　　要：目的:评估重度吸烟者的卒中风险,为优化公共卫生预防策略提供参考。方法:基于NHANES 2017年-2020年相关数据,通过Lasso回归进行特征选择,随后使用随机森林、XGBoost、LightGBM等7种机器学习算法及堆叠算法进行建模。以10折交叉验证评估模型性能,并进行决策曲线分析(DCA)和临床影响曲线(CIC)评估,使用SHAP值增强模型的可解释性。结果:堆叠模型在测试集上表现最佳,AUC值为0.764 5,能够有效区分高卒中风险与低卒中风险个体;训练集上AUC值为0.753 3,证实了模型在训练过程中的稳定性。DCA和CIC评估显示,模型在多个临床决策阈值下提供显著净效益;SHAP值分析显示心脏病史和乙肝疫苗接种等关键变量对预测结果的贡献。结论:机器学习技术可有效预测重度吸烟者的卒中风险,为个性化预防策略提供科学依据,显示了数据驱动模型在疾病预防中的潜力。Objective To assess the stroke risk in heavy smokers and provide reference for optimizing public health prevention strategies.Methods Based on relevant data of NHANES from 2017-2020,feature selection was performed by Lasso regression,followed by modeling with seven machine learning algorithms including Random Forest,XGBoost,LightGBM and stacking algorithm.Model performance was evaluated using 10-fold cross-validation,and decision curve analysis(DCA)and clinical impact curve(CIC)analysis were conducted.SHAP values were used to enhance model interpretability.Results The stacking model performed best on the test set,with an AUC of 0.7645,effectively distinguishing between individuals with high and low stroke risks.The AUC value on the training set was 0.7533,confirming the model's stability during training.DCA and CIC analyses demonstrated that the model provided significant net benefits at multiple clinical decision thresholds.SHAP value analysis showed the contribution of key variables such as history of heart disease and hepatitis B vaccination to the prediction.Conclusion Machine learning can effectively predict stroke risk in heavy smokers,providing scientific basis for personalized prevention strategies.The study demonstrates the potential of data-driven models in disease prevention.

关 键 词：机器学习 重度吸烟 卒中 疾病风险评估 

分 类 号：R743.3[医药卫生—神经病学与精神病学] TP181[医药卫生—临床医学] R319[自动化与计算机技术—控制理论与控制工程]