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对冠状动脉疾病预测的比较研究:传统的QRISK3与增强的机器学习模型相比,与粒子群优化算法相结合

Wigaviola Socha Purnamaasri Harmadha^1,2, Dennis Wang^3,4,5, Mohsin Masood^1,6

¹National Heart and Lung Institute, Imperial College London, London, UK.

|October 31, 2025

概括

与QRISK3.3相比,通过粒子群优化优化的机器学习模型显著改善了冠状动脉疾病 (CAD) 的预测. 这种方法增强了个性化预防策略的风险分层.

科学领域:

心血管疾病的研究研究.
医疗保健中的人工智能
公共卫生领域的预测建模.

背景情况:

冠状动脉疾病 (CAD) 是全球主要的死亡原因.
准确的风险分层对于初级CAD预防至关重要.
像QRISK3这样的现有工具可能会高估风险,导致治疗决策低于最佳水平.

研究的目的:

为了评估机器学习模型的性能与粒子集群优化 (PSO) 结合用于CAD风险预测.
将这些混合模型的预测准确性与已建立的QRISK3得分进行比较.
确定用于识别患有CAD高风险个体的优质方法.

主要方法:

利用英国生物银行数据集,包括348,015名年龄在24-84岁之间的参与者.
开发和评估了各种机器学习模型 (物流回归,决策树,随机森林,天真贝叶斯,梯度提升) 与PSO优化.
采用 4:1 的训练测试数据分割,并使用接收器操作特征分析 (曲线下的面积 - AUC) 评估模型性能.

主要成果:

QRISK3预测模型的AUC值为0.6113.3
与PSO集成的梯度提升模型实现了显著更高的AUC,为0.7258.8.
在348,015名参与者中,6.64%的人在10年内被诊断出患有CAD.

结论:

与PSO优化的混合机器学习模型显示,与QRISK3.3相比,CAD的预测性能优于QRISK3.
这些先进的模型可以更有效地识别高风险个体,以进行有针对性的预防干预.
调查结果支持实施个性化预防策略,并为CAD制定明智的公共卫生政策.

关键词:

冠状动脉疾病冠状动脉疾病全球疾病负担全球疾病负担风险因素风险因素

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