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阻止中国老年人长寿的因素有哪些:一项机器学习研究

Shiyin Xiao^1,2, Yajie Bi^1,2, Wei Chen^3,4

¹School of Psychology, Guizhou Normal University, Guiyang, China.

|July 22, 2024

概括

机器学习模型使用8个关键因素预测中国老年人死亡率,包括日常活动和心理性. 更简单的模型保持了预测性能,有助于健康的长寿和终身护理决策.

科学领域:

老年学是一门学科.
生物统计学生物统计学
医疗保健中的机器学习

背景情况:

由于老年人寿命减少,自我实现和社会贡献的时间有限.
传统的统计方法难以分析多种影响因素,并建立有效的预测模型来预测老年人长寿.

研究的目的:

开发和验证用于预测中国老年人死亡风险的机器学习模型.
确定影响老年人长寿的关键因素,并开发简化预测模型.

主要方法:

利用了来自中国长度健康长寿调查 (CLHLS) 队列 (2008-2018,2014-2018) 的数据.
过并集成了16个功能,应用了五种机器学习算法:弹性网回归 (ENR),决策树 (DT),随机森林 (RF),K-最近邻居 (KNN) 和极端梯度提升 (XGBoost).
通过内部和时间验证评估模型性能,然后根据变量重要性开发更简单的模型.

主要成果:

XGBoost模型表现出优异的性能,AUC为0.788 (内部) 和0.806 (时间验证).
确定了八个关键预测因素:日常生活中的工具性活动 (IADL),休活动,婚姻状况,性别,日常生活活动 (ADL),认知功能,植物性饮食指数 (PDI) 和心理弹性.
使用这些八个特征开发的简单模型在两个验证集中保留了预测性能.

结论:

八个关键因素有效预测中国老年人死亡率,使得创建一个简单的,高性能机器学习模型.
这些发现可以指导未来的研究,促进健康的长寿,并为老年人护理提供实用干预措施,包括终身规划.

关键词:

在 ADL 和 ADL 之间.在IADL中,它是IADL.长寿长寿是一个问题.机器学习机器学习年龄较大的成年人.公共投资投资公司 (PDI)

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