预测和解释老年人的生活满意度使用基于树的集合模型和SHAP:来自数字沟调查的证据
Haiyan Kong1,2, Hualong Fang3, Guihua Zhang1,2
1School of Business, Xinyang Normal University, Xinyang, China.
PloS one
|December 16, 2025
概括
年龄较大的成年人.
科学领域:
- 老年学是指老年学的学科.
- 数字包容研究 数字包容研究
- 计算社会科学 计算社会科学
背景情况:
- 数字化转型加剧了数字沟,不成比例地影响了老年人.
- 数字接入和识字障碍影响着老龄化人口的生活质量和福祉.
- 了解这些障碍对于促进数字公平和提高老年人的生活满意度至关重要.
研究的目的:
- 使用机器学习预测和识别影响老年人生活满意度的关键数字沟因素.
- 在这种情况下,评估基于树的集体学习算法的预测性能和可解释性.
- 提供数据驱动的洞察力,了解数字沟与老龄人口主观福祉之间的关系.
主要方法:
- 利用了来自韩国"2023年数字信息沟调查报告"的原始数据.
- 使用XGBoost进行特征重要性分析,以确定15个关键预测变量.
- 应用了SHAP以获得解释性见解,并比较了随机森林,XGBoost,LightGBM和CatBoost模型的预测性能.
主要成果:
- 技术自我效能,数字素养,社会资本,人工智能服务感知和收入显著预测了老年人的生活满意度.
- 在测试的集体学习算法中,CatBoost模型表现出最高的预测准确度.
- 识别了影响生活满意度的数字信息因素的结构配置.
结论:
- 合体学习算法有效地预测了影响老年人生活满意度的数字沟因素.
- 调查结果为增强老年人数字化包容性和福祉提供了实际意义.
- 这项研究展示了一种新的方法论方法,用于使用机器学习来解决复杂的社会问题.
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