基于机器学习的孤独风险评估模型的开发在中国老年人中:基于中国纵向健康寿命调查的横截面研究
Youbei Lin1, Chuang Li1, Xiuli Wang2
1Jinzhou Medical University, School of Nursing, Jinzhou City, Liaoning Province, 121001, China.
BMC geriatrics
|November 15, 2024
概括
孤独影响了23.4%的中国老年人. 机器学习模型,特别是多层感知器,有效地评估孤独风险,婚姻状况是关键因素.
科学领域:
- 老年学是一门学科.
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 全球老龄化趋势加剧了老年人的孤独感,对心理和身体健康产生了负面影响.
- 由于定义不一致,传统的孤独度表可能会有偏见.
- 机器学习为改善孤独评估和风险建模提供了新的方法.
研究的目的:
- 开发和评估用于评估老年人孤独风险的机器学习模型.
- 确定与老年人孤独相关的关键因素.
主要方法:
- 利用了来自2018年中国纵向健康长寿调查的约16000名参与者 (65岁以上) 的数据.
- 开发了七个机器学习模型 (逻辑回归,回归,SVM,KNN,决策树,随机森林,多层感知子) 使用R 4.4.1.1.
- 评估模型使用ROC曲线,准确性,精度,回忆,F1分数和AUC.
主要成果:
- 在中国老年人中,孤独的发病率为23.4%.
- 确定了与孤独相关的15个因素.
- 多层感知子模型在评估孤独风险方面表现出很高的有效性,这是由于其非线性映射能力.
结论:
- 孤独影响了中国23.4%的老年人.
- 婚姻状况被确定为所有时间框架中预测孤独的最重要因素.
- 从未结婚,丧偶,离婚或分居的人比已婚的人更容易感到孤独.
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