使用机器学习构建预测模型并分析自杀念头的因素,重点关注老年人口
Hyun Woo Jung1,2, Jin Su Jang3
1Department of Health Administration, Graduate School, Yonsei University, Wonju, Republic of Korea.
PloS one
|July 22, 2024
概括
韩国考虑自杀的老年人是一个公共卫生问题. 机器学习模型确定了家庭收入,身体健康和心理健康状况是该人口中自杀念头的关键预测因素.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 计算精神病学是一种计算精神病学.
背景情况:
- 韩国老年人自杀是严重的公共卫生问题.
- 了解自杀念头对于老年人及时干预至关重要.
- 预测建模可以识别有风险的个人,以提供有针对性的支持.
研究的目的:
- 开发和评估机器学习模型,用于预测韩国老年人的自杀念头.
- 确定与自杀念头相关的关键社会经济,行为和健康相关因素.
- 为了比较各种机器学习算法和物流回归的性能.
主要方法:
- 使用了6个机器学习算法和物流回归.
- 采用分层建模方法,在三个模型中结合了社会经济,行为,身体健康和心理健康因素.
- 模型匹配与机器学习和物流回归分析进行了比较.
主要成果:
- 梯度增强算法在预测自杀想法方面表现出卓越的表现.
- 关键预测因素包括家庭收入五分位数,主观健康状况,口腔健康,运动能力,焦虑和抑郁.
- 经济和住宅脆弱性与增加的自杀想法有显著的相关性.
结论:
- 机器学习模型有效地预测了老年人的自杀念头.
- 识别社会经济和健康弱点可以指导有针对性的自杀预防策略.
- 层次化的方法有助于精确确定易受干预的脆弱人群.
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