使用合成少数过量采样技术和极端梯度增强的中风预测
Mahdi Hassan1, Hamid Nasiri2, Mona Esmaeili3
1Electrical and Computer Engineering Department, Semnan University, Semnan, Iran.
Computer methods in biomechanics and biomedical engineering
|October 8, 2025
概括
早期预测中风对于预防至关重要. 机器学习模型,特别是XGBoost,实现了高精度 (97.26%),将年龄和血压确定为临床决策支持的关键风险因素.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
- 心血管疾病研究研究
背景情况:
- 卒中是全球主要的死亡原因,需要准确的早期预测模型.
- 可解释模型对于指导中风预防和临床护理策略至关重要.
研究的目的:
- 开发和评估高精度,可解释的机器学习模型,用于早期中风风险预测.
- 用先进的解释性技术识别中风的关键预测因素.
主要方法:
- 使用Kaggle中风数据集,应用SMOTE用于类不平衡.
- 训练并比较了XGBoost,随机森林,LightGBM,CatBoost和支持矢量机 (SVM) 模型.
- 采用十倍交叉验证来进行可靠的绩效评估,并采用SHAP来进行模型可解释性.
主要成果:
- XGBoost以97.26%的准确度表现出卓越的性能,超过了其他车型和之前的基准.
- 沙普利增量解释 (SHAP) 算法将年龄和高血压/血压确定为主要的中风预测因素.
- 该研究提供了全球特征重要性排名和案例级预测见解.
结论:
- 开发的管道为中风风险预测提供了一种实用和可解释的方法.
- 实现了最先进的性能,使该模型适合临床决策支持系统.
- 强调机器学习在加强心血管疾病预防和管理方面的潜力.
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