哪些是成功预测衰老的最佳方法? 包装,提升,还是简单的机器学习算法?
Razieh Mirzaeian1, Raoof Nopour2, Zahra Asghari Varzaneh3
1Department of Health Information Management, Shahrekord University of Medical Sciences, Shahrekord, Iran.
Biomedical engineering online
|August 29, 2023
概括
机器学习通过识别关键影响因素,准确地预测成功的衰老 (SA). 随机森林算法在这种预测建模中表现出卓越的性能,为改善老年护理提供了有价值的工具.
科学领域:
- 老年学与公共卫生
- 计算健康科学 计算健康科学
背景情况:
- 全球预期寿命的增加需要关注成功的衰老 (SA) 作为健康质量指标.
- SA是一个复杂的,多维的概念,使其定义和测量具有挑战性.
- 确定影响SA的因素对于开发有效干预措施至关重要.
研究的目的:
- 确定影响老年人成功衰老 (SA) 的关键因素.
- 开发和评估用于预测SA的机器学习 (ML) 模型.
- 为了确定最有效的SA预测ML算法.
主要方法:
- 通过采访收集的数据来自伊朗阿巴丹的1465名60岁以上的成年人 (2021-2022年).
- 二元物流回归用于识别与SA相关的重要因素.
- 八个ML算法 (包括随机森林,XGBoost,SVM) 被训练并对SA预测准确性进行评估.
主要成果:
- 发现44个因素与成功的衰老 (SA) 有着显著的关系.
- 随机森林 (RF) 算法在预测SA方面取得了最高的性能.
- 射频模型实现了0.94准确度,0.95灵敏度,0.94特异性和0.94F-score.
结论:
- 随机森林算法在预测成功的衰老方面明显优于其他ML方法.
- 开发的ML模型为老年学家,医疗保健提供者和决策者提供了可靠的工具.
- 这些模型可以帮助改善老年人群的健康结果.
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