基于机器学习的冠状动脉疾病预测使用临床和行为数据:一项比较研究
Abdulkadir Çakmak1, Gülşah Akyilmaz2, Aybike Gizem Köse3
1Department of Cardiology, Faculty of Medicine, Amasya University, Amasya 05200, Türkiye.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
机器学习模型通过整合临床和行为数据,准确地检测冠状动脉疾病 (CAD). k-最近邻居 (k-NN) 模型显示了最高的准确性,突出了改善早期诊断的潜力.
科学领域:
- 心脏病学和人工智能的人工智能
- 生物医学数据科学 生物医学数据科学
- 预测分析在医疗保健中的应用
背景情况:
- 冠状动脉疾病 (CAD) 是一个主要的全球健康问题,需要准确的早期诊断.
- 机器学习 (ML) 为整合复杂的患者数据提供了先进的工具,以提高诊断能力.
- 多维患者数据集成是提高CAD风险分层和临床管理的关键.
研究的目的:
- 开发和比较用于早期CAD诊断的监督ML算法.
- 在CAD预测中评估人口统计,人体测量,生化和心理社会参数的有效性.
- 确定最有效的ML模型来区分CAD患者与对照者.
主要方法:
- 对300名成年患者 (165名患有CAD,135名对照) 的回顾性分析.
- 利用了一个数据集,包括21个生化标志物,身体成分和饮食行为得分.
- 使用十倍交叉验证训练和评估了六个ML算法 (k-NN,SVM,ANN,LR,NB,DT).
主要成果:
- k-NN模型获得了最高的精度 (98.33%) 和AUC (0.99),其次是SVM和ANN.
- 患有CAD的患者显示葡萄糖,甘油三,LDL胆固醇和腹部肥胖率升高,维生素B12的含量较低.
- 情绪性饮食等心理社会因素对模型表现的影响有限.
结论:
- 监督的ML模型,特别是k-NN,SVM和ANN,在CAD检测中显示出高精度.
- 整合多样化的临床和行为数据增强了超越传统生物标志物的CAD预测.
- 这种方法有望改善早期CAD诊断和患者风险分层.
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