使用机器学习模型预测消防员的冠状动脉分数
Mingyue Li1,2, Jiali Han1,3, Carolyn Muegge4,5
1Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA.
Health informatics journal
|October 17, 2025
概括
机器学习模型,特别是XGBoost,在预测消防员的冠状动脉 (CAC) 中显示出高准确度. 这有助于对这个高风险职业群体的早期发现和预防策略.
科学领域:
- 心血管疾病预测预测.
- 职业健康 职业健康 职业健康 职业健康
- 机器学习在医疗保健中的应用
背景情况:
- 消防员面临心血管疾病的风险增加.
- 早期检测冠状动脉 (CAC) 对预防策略至关重要.
- 传统的风险预测模型在这个人群中可能存在局限性.
研究的目的:
- 开发和比较用于消防员CAC预测的机器学习 (ML) 模型.
- 评估ML模型与二进制物流回归 (BLR) 的交叉验证性能.
主要方法:
- 利用了416名消防员的健康记录.
- 使用心脏计算机断层扫描和Agatston分数评估CAC.
- 开发并比较了XGBoost,随机森林,SVM,天真贝叶斯和KNN模型与BLR,使用17个临床和生活方式变量.
主要成果:
- 年龄,葡萄糖,单细胞百分比和缩血压与CAC有积极的关联.
- 水平,GFR和最大氧量与CAC相反相关.
- XGBoost获得了最高的交叉验证AUC (0.770),超过了其他ML模型和BLR (0.658).
结论:
- 机器学习算法,特别是XGBoost,在预测消防员CAC方面非常有效.
- 这些模型增强了早期检测和预防策略的心血管健康在这个职业群体.
- 使用先进的预测工具进行主动的健康管理对消防员至关重要.
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