在全球流行病中,通过可解释的集体学习探索幸福因素
Md Amir Hamja1, Mahmudul Hasan2, Md Abdur Rashid3
1Department of Statistics, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
PloS one
|January 2, 2025
概括
机器学习模型准确地预测了幸福分数,揭示了人均GDP作为一个关键因素. 在疫情期间,社会支持成为整体幸福的最关键因素.
科学领域:
- 社会科学 社会科学 社会科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 幸福是一种影响整体幸福感的多方面的状态.
- 世界幸福报告 (WHR) 提供了诸如人均GDP,社会支持和预期寿命等关键指标.
- 了解影响幸福的因素对于社会进步至关重要.
研究的目的:
- 使用机器学习 (ML) 和深度学习 (DL) 算法预测幸福度.
- 分析个别变量对幸福指数的影响.
- 评估COVID-19大流行对幸福指标的影响.
主要方法:
- 开发了两个整体ML/DL模型:混合RGMLL和堆叠LRGR.
- 使用诸如回归,梯度提升,多层感知子,长短期记忆,线性回归和随机森林等技术.
- 可解释的人工智能 (XAI) 的应用用于详细分析.
主要成果:
- 混合RGMLL模型实现了高预测精度 (R2=85%).
- "人均GDP"被确定为幸福度评分的主要驱动因素.
- 在COVID-19大流行期间",社会支持"成为最重要的因素,其次是"健康预期寿命"和"人均GDP".
结论:
- ML和DL模型有效地预测了幸福及其决定因素.
- 疫情凸显了社会支持在维持幸福的关键作用.
- 研究结果提供了在危机期间增强幸福感和恢复力的见解.
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