在中国东部社区生活的老年人中,脆弱的决定因素和风险预测模型
Lin Qi1,2, Jianyu Liu2, Xuhui Song2
1College of Management, Hainan Medical University, Haikou, China.
Frontiers in public health
|March 26, 2025
概括
机器学习模型预测了老年人的脆弱风险. 随机森林和XGBoost显示出卓越的准确性,使得早期检测和个性化干预能够改善生活质量.
科学领域:
- 老年学是一门学科.
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 脆弱性是社区居住的老年人的一个重要问题,影响生活质量.
- 早期检测和干预对于缓解脆弱性进展至关重要.
研究的目的:
- 开发和评估用于预测中国东部老年人脆弱风险的机器学习模型.
- 确定导致这一群体脆弱的关键因素.
- 促进高风险个体的早期识别,以进行量身定制的干预.
主要方法:
- 中国东部1,263名社区老年人 (≥60岁) 参与了这项研究.
- 使用蒂尔堡脆弱性指标 (TFI) 评估脆弱性.
- 使用决策树,随机森林和XGBoost构建预测模型,并使用AUC和ROC曲线进行评估.
主要成果:
- 64.77%的参与者表现出身体虚弱.
- 关键的因素包括BMI,生活安排,访问频率和吸烟情况.
- 随机森林和XGBoost模型显示出比决策树更高的准确性和适用性.
结论:
- 机器学习,特别是随机森林和XGBoost,有效地预测了老年人的脆弱性.
- 早期识别高风险个体可以进行个性化干预,以减缓脆弱性和提高生活质量.
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