在桑给巴尔预测基于设施的交付:机器学习算法对敌对攻击的脆弱性
Yi-Ting Tsai1, Isabel R Fulcher2,3, Tracey Li4
1Department of Biostatistics, Harvard Chan School of Public Health, Boston, USA.
Heliyon
|May 26, 2023
概括
机器学习模型识别有风险的母亲在家分娩是脆弱的对抗性攻击. 数据操纵,特别是以前的交付地点,可以改变预测,影响撒哈拉以南非洲的计划有效性.
科学领域:
- 机器学习在公共卫生中的应用
- 母亲健康干预措施 母亲健康干预措施
- 医疗保健中的数据安全性
背景情况:
- 社区卫生工作者 (CHW) 领导的项目改善了撒哈拉以南非洲的母亲健康.
- 移动设备的采用使实时机器学习能够识别高风险怀孕.
- 敌对攻击或伪造数据对这些预测模型的准确性构成威胁.
研究的目的:
- 为了评估机器学习算法对对抗攻击的脆弱性.
- 评估数据操纵对预测家庭分娩风险的影响.
主要方法:
- 利用了来自桑给巴尔Uzazi Salama计划 (2016-2019) 的数据.
- 开发了一个使用 LASSO 调整后勤回归的预测模型.
- 应用于对各种输入变量 (二进制,分类,顺序,连续) 的"一次性 (OAT) "对抗性攻击.
主要成果:
- 敌对攻击显著改变了预测结果.
- "之前的交付地点"变量显示出最严重的漏洞.
- 当操纵该变量在设施和家庭交付之间时,预测变化为55.65%和37.63%.
结论:
- 这项研究强调了孕产妇健康预测算法对敌对操纵的易感性.
- 了解这些漏洞对于开发强大的数据监控策略至关重要.
- 确保数据完整性对于社区卫生工作者有效准风险女性至关重要.
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