乌干达的统一的口病数据集:在不同分布下评估机器学习预测性能的退化
Geofrey Kapalaga1, Florence N Kivunike1, Susan Kerfua2
1Department of Information Technology, College of Computing and Information Sciences, Makerere University, Kampala, Uganda.
Frontiers in artificial intelligence
|August 15, 2024
概括
在乌干达创建有关口病 (FMD) 的统一数据集显示,机器学习模型的性能因气候变化而下降. 需要新的方法来提高口热爆发预测的准确性.
科学领域:
- 兽医流行病学 兽医流行病学
- 机器学习在动物健康中的应用.
- 气候变化影响评估 气候变化影响评估
背景情况:
- 乌干达缺乏统一的数据集,用于基于机器学习的口病 (FMD) 疫情预测,这阻碍了准备工作.
- 用于口疾病预测的机器学习模型对环境变化敏感,特别是气候变化带来的降雨和温度变化.
研究的目的:
- 通过整合不同的数据来源,为乌干达创建一个统一的口病数据集.
- 在非静止环境条件下评估七个机器学习模型的性能退化.
- 鉴于气候变化,确定可靠的口热疫情预测模型.
主要方法:
- 数据统一涉及整合不同的来源,平均归算,重复删除,可视化和合并技术.
- 七个机器学习模型被训练并使用准确度,AUC,回忆,精度和F1分数进行评估.
- 数据增强 (边界SMOTE) 用于解决口病数据集中显著的阶级不平衡.
主要成果:
- 统一数据集显示出显著的阶级不平衡,非爆发者比爆发者更多.
- 随机森林与边界SMOTE在静止条件下实现了高性能 (92%准确率,0.97AUC).
- 所有模型在不同降雨量和温度分布下都显示出显著的性能恶化,随机森林的准确性降至46%.
结论:
- 为乌干达创建一个统一的口病数据集是改善疫情预测的关键一步.
- 气候变化大大降低了现有的机器学习模型对口热的预测性能.
- 开发新方法至关重要,以减轻分布变异对口热爆发预测准确性的影响.
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