为老年人开发,验证和运输几种基于机器学习的,非基于运动的VO2max预测模型
Benjamin T Schumacher1, Michael J LaMonte2, Andrea Z LaCroix1
1Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA.
机器学习 (ML) 模型准确地预测了老年人最大的氧气摄入量 (VO2max),为健康的衰老提供了重要信号. 这些预测显示出与死亡率的关联,尽管需要进一步验证.
科学领域:
- 老年学是一门学科.
- 心血管健康 呼吸系统健康
- 机器学习应用 机器学习应用
背景情况:
- 在大规模研究中,测量最大氧吸收量 (VO2max) 通常是不切实际的.
- 现有的VO2max非运动预测方程是有限的,特别是在老年人中.
- 机器学习 (ML) 提供了改善VO2max预测的潜力.
研究的目的:
- 开发和验证用于预测老年人VO2max的ML算法.
- 为了比较不同ML模型的性能.
- 评估这些算法的可转移性及其与死亡率的关联.
主要方法:
- 利用了来自巴尔的摩老龄化纵向研究 (BLSA) 的数据,其中有1080名参与者.
- 训练了各种ML算法,包括LASSO,XGBoost,随机森林和SVM.
- 开发了使用所有可用的变量和老化队列中常见的子集的模型.
主要成果:
- 机器学习模型实现了低根平均平方误差,表明了良好的预测准确性 (例如,LASSO和XGBoost在3.4mL/kg/min).
- 测量VO2max表明与死亡风险有明显的反向关系.
- 预测的VO2max显示出与死亡率类似的趋势,但在调整后缺乏稳定性.
结论:
- 与更简单的方法相比,ML提高了VO2max预测的准确性.
- 测量VO2max是老年人死亡率的重要预测指标.
- 需要进一步的研究来验证基于ML的VO2max预测,以促进健康的衰老.
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