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使用极端梯度增强对冠状动脉疾病进行预测模型的构建和验证

Zheng Zhang^1,2, Binbin Shao³, Hongzhou Liu^2,4

¹Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, People's Republic of China.

Journal of inflammation research

|July 8, 2024

概括

一个新的机器学习模型使用XGBoost准确地识别冠状动脉疾病 (CAD) 风险,帮助早期检测和社区查中的个性化患者管理.

科学领域:

心脏病学心脏病学
机器学习机器学习
生物统计学生物统计学

背景情况:

早期识别冠状动脉疾病 (CAD) 对于降低死亡率至关重要.
对于基于社区的CAD风险评估,需要敏感,具体,成本效益和非侵入性指标.

研究的目的:

开发和验证用于准确CAD风险评估的机器学习模型.
在大量人口群体中确定用于预测CAD的关键指标.

主要方法:

利用绝对最小收缩和选择操作员 (LASSO) 回归来识别3112名CAD患者和3182名对照者的显著特征.
为CAD风险预测构建并比较了四种机器学习模型 (逻辑回归,SVM,RF,XGBoost).
使用ROC和PR曲线,SHAP分析,校准曲线和DCA评估模型性能,并进行外部验证.

主要成果:

在发现队列中,XGBoost模型表现出优异的预测性能,AUC为0.988 (灵敏度94.6%,特异性94.6%).
校准曲线证实了预测和观察结果之间的良好一致性.
外部验证产生了强大的歧视性性能 (AUC为0.953和0.935).

结论:

开发的XGBoost模型对临床实践具有高度的信息意义.
该模型支持初级预防策略和针对CAD患者的量身定制管理.

关键词:

在XGBoost中使用.冠状动脉疾病是一种冠状动脉疾病.机器学习是机器学习.预测模型是一个预测模型.首要预防首要预防

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