使用机器学习模型预测饮用水质量:公共卫生护理方法
Gözde Özsezer1,2, Gülengül Mermer3
1Çanakkale Onsekiz Mart University Faculty of Health Sciences Department of Public Health Nursing, Çanakkale, Turkey.
Public health nursing (Boston, Mass.)
|November 24, 2023
概括
机器学习模型准确地预测饮用水质量. XGBoost和Random Forest表现出卓越的表现,帮助公共卫生护士确保获得清洁水和保护社区健康.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 饮用水质量对公共健康至关重要.
- 预测建模可以提高水质监测.
- 公共卫生护理在确保获得安全饮用水方面发挥着至关重要的作用.
研究的目的:
- 使用机器学习 (ML) 模型预测饮用水质量.
- 评估各种ML算法的性能,以预测水质.
- 将公共卫生护理方法应用于水质评估.
主要方法:
- 使用了"水质数据集",包括2400个物理和化学测量.
- 采用合成少数群体过量采样技术 (SMOTE) 的数据处理和通过十倍交叉验证进行超参数调整.
- 对比了七个ML算法:逻辑回归,K-最近邻居,支持向量机,随机森林,XGBoost,AdaBoost分类器和决策树,使用准确度,精度,回忆,F1得分和AUC指标.
主要成果:
- 在所有指标中,XGBoost和Random Forest被确定为表现最佳的分类模型.
- 在所有基于p值分析的ML算法中观察到显著差异.
- 对于XGBoost和随机森林,被接受了零假设 (H0),表明实际和预测值之间没有显著差异.
结论:
- 机器学习模型是预测饮用水质量的有效工具.
- XGBoost和Random Forest为水质分类提供了强大的性能.
- 在公共卫生护理中整合ML可以改善获得清洁水的机会,并保护个人健康.
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