一个模型预测老年人群主观福祉的两年变化
Isotta Trescato1, Chiara Roversi1, Martina Vettoretti1
1Department of Information Engineering, University of Padova, Padova (PD), Italy.
BMC medical informatics and decision making
|November 8, 2023
概括
预测老年人的健康状况至关重要. 一个随机的森林模型准确地预测了两年内主观福祉 (CASP-12规模) 的变化,确定了抑郁症和财务困难作为关键预测因素.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 计算统计学 计算统计学
背景情况:
- 全球人口正在老龄化,这给公共卫生带来了挑战,特别是在老年人的主观福祉方面.
- 预测老年人对感知幸福感的变化对于有针对性的干预措施至关重要.
- 该研究重点关注50岁以上的个人,以了解和预测福祉变化.
研究的目的:
- 开发一个预测模型,用于50岁以上个体的感知幸福感的两年变化.
- 确定影响主观幸福感变化的关键变量,用CASP-12尺度测量.
- 评估不同机器学习模型的预测能力.
主要方法:
- 使用的欧洲SHARE项目数据 (N=9422) 包括人口,健康,社会和财务变量.
- 使用CASP-12尺度测量主观幸福感,结果被定义为两年内恶化/不恶化.
- 比较后勤回归,LASSO规范后勤回归和随机森林模型,通过准确性,AUC和F1得分来评估性能.
主要成果:
- 随机森林模型以65%的准确性,AUC为0.659和F1得分为0.710.0的最佳表现.
- 所有模型都显示了跨学科和时间的概括性.
- 基线CASP-12得分,抑郁症的存在和财务困难是幸福感变化最重要的预测因素.
结论:
- 随机森林是预测老年人福祉变化的合适模型,尽管物流回归变体是可行的替代方案.
- 识别抑郁症和财务困难等预测因素,可以制定积极的公共卫生战略.
- 这些模型为理解和潜在地改善老龄化人口的主观福祉提供了基础.
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