心血管风险因素和治疗暴露对心血管事件发生率的影响:使用机器学习算法进行评估
Sara Castel-Feced1,2,3, Sara Malo1,2,3, Isabel Aguilar-Palacio1,2,3
1Microbiology, Pediatrics, Radiology, and Public Health, University of Zaragoza, Zaragoza, Spain.
机器学习模型准确地预测心血管事件 (CVEs),强调年龄是关键风险因素. 治疗坚持显著影响CVE风险,使个性化心血管预防策略成为可能.
科学领域:
- 心脏病学 心脏病学
- 生物统计学 生物统计学
- 计算医学是一种计算医学.
背景情况:
- 传统的心血管风险评分系统在个性化医学的局限性.
- 机器学习 (ML) 为预测心血管事件 (CVEs) 提供了先进的能力.
- 了解心血管风险因素 (CVRF) 的影响对于有效预防至关重要.
研究的目的:
- 评估用于CVE预测的ML算法.
- 分析CVRF和治疗坚持对CVE预测的影响.
- 为了比较不同ML模型在预测CVEs方面的性能.
主要方法:
- 一项对3746名男性工人的队列研究,使用人口数据.
- 应用XGBoost,随机森林和天真贝叶斯 (NB) ML算法.
- 分析CVRF (年龄,身体状况,高胆固醇血症,高血压,糖尿病) 具有和没有治疗暴露变量.
主要成果:
- 在所有模型中,年龄始终被确定为CVE发病率最有影响力的变量.
- 治疗暴露 (坚持) 显然比其他CVRF更有影响力,因模型而异.
- 随机森林在包括治疗暴露时表现出最高的准确性 (F1得分为0.84).
结论:
- 机器学习算法可以在现有系统之外增强心血管风险预测.
- 坚持治疗是CVE风险的一个关键因素,但往往被低估.
- 个性化心血管预防模型可以在初级保健中使用这些ML方法为特定人群开发.
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