基于机器学习的预测模型,用于预测血清γKlotho水平对冠心病敏感性的影响
Zi-Tong Guo1, Xiao-Lin Yu2, Hui Cheng2
1Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, People's Republic of China.
血清γKlotho水平是一种与冠心病 (CHD) 风险积极相关的新生物标志物. 一个机器学习模型,特别是随机森林 (RF),在临床环境中预测心血管疾病风险方面表现有前途.
科学领域:
- 心脏病学 心脏病学
- 生物标志物 生物标志物
- 医疗保健中的机器学习
背景情况:
- 冠心病 (CHD) 仍然是全球死亡的主要原因.
- 识别新的生物标志物和改善CHD风险的预测模型对于早期干预至关重要.
- 血清γKlotho已经成为影响心血管健康的潜在因素.
研究的目的:
- 研究血清γKlotho水平与患冠状动脉心脏病 (CHD) 的风险之间的关联.
- 开发和验证用于预测心脏病风险的机器学习 (ML) 模型.
- 评估血清γKlotho作为心血管疾病生物标志物的临床效用.
主要方法:
- 分析了1435名受试者,随机分配到培训 (70%) 和验证 (30%) 组.
- 使用单变量和最小绝对收缩和选择操作员 (LASSO) 回归来确定慢性心脏病的独立风险因素.
- 开发和评估了9个ML模型,选择了表现最好的模型 (随机森林 - RF) 用决策曲线分析 (DCA) 进行验证.
主要成果:
- 与心脏病风险独立相关的关键因素包括年龄,血清gKlotho水平,LDL-C,性别,糖尿病,高血压和吸烟状况.
- 与其他八种ML模型相比,随机森林 (RF) 模型表现出优异的性能.
- 验证证实了开发的RF模型用于CHD风险预测的有希望的临床适用性.
结论:
- 血清 γKlotho 是一种与冠心病 (CHD) 风险正相关的新生物标志物.
- 随机森林 (RF) 模型提供了一种可靠且准确的方法来预测心血管疾病风险.
- 射频模型非常适合临床应用,用于评估和管理心脏病风险.
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