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使用随机森林分类器预测老年虐待

Qiyan Zeng1, Yannan Wang1, Fuming Zhao2

  • 1Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang A&F University, Hangzhou City, China.

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概括
此摘要是机器生成的。

儿童的财务稳定是虐待老人的首要预测因素,其他因素,如父母的健康和家庭动态也起着重要作用. 机器学习准确地识别了这些关键的风险因素.

关键词:
虐待老年人 虐待老年人机器学习是机器学习.老年人年长的人.随机的森林随机的森林

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科学领域:

  • 老年学是指老年学的学科.
  • 社会科学 社会科学 社会科学
  • 公共卫生 公共卫生

背景情况:

  • 虐待老年人是一个重要的社会问题,其根本原因复杂.
  • 了解导致不同类型老年虐待的异质因素对于有针对性的干预至关重要.
  • 识别虐待老年人风险因素的传统方法在预测准确性方面存在局限性.

研究的目的:

  • 确定影响家庭中的老年虐待的关键因素.
  • 分析这些因素在不同类型的老年虐待 (财务,身体,心理,忽视) 中如何变化.
  • 应用监督机器学习方法来加强风险因素的识别.

主要方法:

  • 利用了来自中国纵向老龄化社会调查的数据 (n=10,703).
  • 使用随机森林分类器,监督机器学习技术.
  • 根据预测重要性确定并对关键影响因素进行排名.

主要成果:

  • 儿童的经济地位成为虐待老年人的最重要的预测因素.
  • 其他关键因素包括孩子的数量,老年人的健康状况,代际关系和孩子的时间压力.
  • 儿童的经济状况最能预测身体/心理虐待和忽视;儿童人数最能预测经济虐待.
  • 基于家庭的老年人护理服务的影响显示,与老年人虐待有非单一的关系.

结论:

  • 与传统的经济学模型相比,机器学习,特别是随机森林分类,在预测老年虐待方面提供了更高的准确性.
  • 儿童的社会经济和人口特征,以及父母的健康和关系质量,是虐待老人的关键决定因素.
  • 研究结果为制定有效的预防和干预老年虐待策略提供了宝贵的见解.