关于SVM模型用于预测孟加拉国婴儿死亡率的解释性
Md Abu Sayeed1, Azizur Rahman2, Atikur Rahman2
1Department of Statistics and Data Science, Jahangirnagar University, Dhaka, Bangladesh. sayeed@juniv.edu.
Journal of health, population, and nutrition
|October 27, 2024
概括
这项研究使用可解释机器学习 (ML) 来分析孟加拉国的婴儿死亡率. 虽然逻辑回归 (LR) 预测得更好,但可解释支向量机 (SVM) 确定了婴儿死亡的特定风险因素.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 婴儿死亡率仍然是一个关键的全球公共卫生问题,影响社会和经济发展.
- 机器学习 (ML) 模型提供了高的预测性能,但往往缺乏可解释性,阻碍了信任和应用.
- 可解释的ML旨在弥合这一差距,将预测能力与复杂决策的透明解释相结合.
研究的目的:
- 应用先进的可解释机器学习技术来预测和理解影响孟加拉国婴儿死亡率的因素.
- 通过使用可解释支向量机 (SVM) 模型,克服传统的逻辑回归 (LR) 的局限性.
- 为公共卫生政策和家庭咨询服务提供可操作的见解.
主要方法:
- 利用了孟加拉国人口与健康调查 (BDHS) 2017-18年的数据.
- 使用可解释支向量机器 (SVM) 采用全球代用和本地个人条件预期 (ICE) 技术.
- 将SVM与后勤回归 (LR) 进行比较,通过ROC曲线,运行时间和混矩阵评估性能,超过100次排列.
主要成果:
- 后勤回归 (LR) 显示了更高的准确性 (0.9105),但无法预测阳性病例,并且运行时间较慢.
- 可解释的SVM确定了特定的风险因素:正常的BMI,短的分娩间隔,较少污染的燃料,工作的母亲和男婴与更高的婴儿死亡风险有关.
- 用SVM进行的全球代孕分析表明,使用污染燃料的工作母亲或分娩间隔较长的母亲的婴儿死亡风险更高;使用污染燃料的非工作母亲的分娩间隔较短也面临更高风险.
结论:
- 可解释的ML模型,特别是SVM,为婴儿死亡率决定因素提供全球和本地洞察力,有助于临床理解.
- 这些发现为决策者和利益相关者提供了关键信息,以制定有针对性的干预措施并改进家庭咨询策略.
- 可解释的ML增强了预测模型在解决诸如婴儿死亡率等关键公共卫生挑战中的实际应用.
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