增强机器学习和混合组合方法用于冠心病预测
Maurice Wanyonyi1, Zakayo Ndiku Morris1, Faith Mueni Musyoka2
1Department of Mathematics and Statistics, University of Embu, Embu, Kenya.
PloS one
|December 26, 2025
概括
增强的机器学习模型显著提高了冠心病 (CHD) 预测准确度. 这些先进的AI工具为资源有限的环境提供了强大的诊断能力,有助于早期检测和预防工作.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 冠心病 (CHD) 是全球主要的死亡原因,特别是在具有有限诊断资源的低收入和中等收入国家.
- 传统的统计模型与复杂,高维,不平衡的健康数据作斗争,以准确预测心血管疾病.
研究的目的:
- 开发和评估增强的机器学习和混合组合模型,以改进CHD预测.
- 专注于提高诊断性能,可解释性和在资源有限的环境中的应用性.
主要方法:
- 利用来自行为风险因素监测系统的大型,全国代表性的数据集 (253,680人).
- 采用数据预处理,包括规范化和合成少数群体过量采样技术 (SMOTE) 来进行平衡.
- 对比基线模型 (决策树,随机森林,梯度提升,SVM) 与增强版本 (ADNRT,HIRF,PGBM,ESVM) 和整体方法 (堆叠,提升,袋装,贝叶斯模型平均,多数投票).
主要成果:
- 增强型模型在CHD预测中始终表现优于基线模型.
- 修剪渐变增强机 (PGBM) 实现了最高的灵敏度 (90.8%).
- 混合失衡随机森林 (HIRF) 显示出优异的校准和平衡 (AUC = 0.937).
- 堆叠组合模型表现出最佳的整体性能,准确度为87.2%,灵敏度为89.6%,特异性为84.7%,AUC为0.94.
- 校准和学习曲线分析表明,集体模型具有很强的概括性和最小的过拟合性.
结论:
- 混合组合机器学习模型在CHD预测方面明显优于传统分类器.
- 这些模型提供了高精度,稳定性和可解释性,这对于临床决策至关重要.
- 开发的模型为低资源环境中人工智能驱动的诊断提供了一个可扩展的框架,有可能彻底改变CHD早期检测和预防.
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