在死亡率的流行病学研究中应用机器学习算法
George O Agogo1, Henry Mwambi2
1StatsDecide Analytics and Consulting Ltd, P.O Box 17432- 20100, Nakuru, Kenya.
Annals of epidemiology
|January 5, 2025
概括
机器学习有效地识别了死亡风险因素,包括年龄和血压. 结婚和收入等因素与较低的死亡风险有关,有助于针对性干预.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 流行病学研究对于了解死亡风险因素至关重要.
- 机器学习 (ML) 为分析复杂的健康数据提供了强大的工具.
- 识别死亡率预测因素对于公共卫生干预至关重要.
研究的目的:
- 应用和评估各种机器学习算法来识别所有原因死亡风险因素.
- 评估不同ML模型在预测死亡率方面的表现.
- 解释ML模型的发现,以获得可操作的见解.
主要方法:
- 利用国家健康和营养检查调查 (NHANES) 数据 (2009-2016) 与死亡率数据连接到2019年.
- 应用后勤回归,随机森林,k-最近邻居,MARS,SVM,XGBoost和超级学习者ML算法.
- 使用AUC-ROC,灵敏度和NPV评估模型性能,并使用SHapley添加剂解释.
主要成果:
- 机器学习模型实现的AUC-ROC范围从0.80到0.87.
- 超级学习者模型显示了最高的性能 (AUC-ROC:0.87,灵敏度:0.86,NPV:0.98).
- 确定了关键的死亡风险因素:晚年,腰围增加,男性性别和静缩血压. 保护因素包括婚姻,更高的收入和教育.
结论:
- 机器学习是流行病学研究中识别死亡风险因素的宝贵工具.
- 这些发现支持开发个性化,有针对性的干预措施.
- 基于机器学习的洞察力可以增强用于减少死亡率的公共卫生战略.
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