基于机器学习的自杀预测和美国各县自杀脆弱性指数的开发
Vishnu Kumar1, Kristin K Sznajder2, Soundar Kumara3
1Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA. vbk5101@psu.edu.
Npj mental health research
|April 12, 2024
概括
美国的自杀率正在上升. 一个新的机器学习模型使用人口和人口统计数据预测县级自杀脆弱性,帮助有针对性的预防工作.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 计算流行病学计算流行病学
背景情况:
- 自杀在美国是一个显著且日益增长的公共卫生挑战.
- 了解和预测自杀模式对于有效的控制和预防战略至关重要.
研究的目的:
- 分析自杀趋势和2010-2019年美国各县的地理分布.
- 开发和验证用于县级自杀预测的机器学习模型.
- 确定影响自杀率的主要人口特征.
主要方法:
- 利用涵盖2010-2019年所有3140个美国县的公开数据.
- 开发了一个XGBoost机器学习模型,包含17个功能.
- 采用了夏普利添加式解释 (SHAP) 来确定特征的重要性.
主要成果:
- 在许多县观察到自杀率的显著增加;大约25%的自杀率至少增加了10%,12%的自杀率超过了50%.
- 该XGBoost模型实现了高预测准确度,R2值为0.98.
- 总人口, % 非洲裔美国人, % 白人, 平均年龄和 % 女性人口被确定为前五大预测因素.
结论:
- 一个新的自杀脆弱性指数 (SVI) 是使用前5个预测特征开发的.
- 该SVI可以确定美国自杀风险较高的县.
- 该工具支持针对性自杀预防和控制计划的知情决策.
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