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开发基于高级肺癌炎症指数的机器学习模型,用于预测中风和死亡率:一个比较和可解释的研究

Jiaxin Fan¹, Xingzhi Guo², Shuai Cao³

¹Department of Neurology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Xi'an, China; Shaanxi Provincial Clinical Research Center for Geriatric Medicine, Xi'an, China.

Computer methods and programs in biomedicine

|December 10, 2025

概括

高级肺癌炎症指数 (ALI) 与中风风险降低和中风患者死亡率降低有关. 机器学习模型显示,使用ALI预测中风和预后具有前途.

科学领域:

心血管研究研究心血管研究
生物统计学生物统计学
计算生物学计算生物学

背景情况:

先进的肺癌炎症指数 (ALI) 提供了对炎症和营养状况的全面评估.
现有的研究尚未广泛探索ALI在中风患者中的作用.
这项研究调查了ALI与中风风险和死亡率的关联,并开发了预测机器学习 (ML) 模型.

研究的目的:

检查高级肺癌炎症指数 (ALI) 和中风风险之间的关系.
评估ALI与中风患者全因死亡率之间的关联.
开发和解释用于预测中风和患者预后的ML模型.

主要方法:

使用了来自国家健康和营养检查调查 (NHANES) (1999-2018) 的数据.
采用后勤回归,考克斯回归和受限立方线来分析ALI与中风和死亡率的关联.
开发和评估了六个ML模型 (LR,XGBoost,RF,KNN,SVM,DT) 用于预测,使用AUCROC和精度指标,增强了SHAP和Gini的重要性.

主要成果:

较高的ALI与中风风险降低相关.
随着ALI增加,死亡率下降到一个转折点 (ALI = 40.91).
随机森林 (RF) 模型在中风识别 (AUCROC: 0.9657) 和死亡率预测 (AUCROC: 0.7771) 中表现强. 心血管疾病和年龄分别是中风和死亡率的关键预测因素.

结论:

ALI表现出与中风风险的逆剂量反应关系,以及在中风患者中与全因死亡率的"L形"关联.
结合ALI的双解释性射频模型显示了对中风识别和预后预测的潜力.
这项研究强调ALI是心血管健康和中风结果的相关生物标志物.

关键词:

晚期肺癌的炎症指数是什么可以解释性解释性机器学习机器学习预测预测预测一次性中风,中风.

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