利用机器学习中的AHP和转移学习来更好地预测传染病爆发
Reham Abdallah1, Sayed Abdelgaber2, Hanan Ali Sayed3
1Information Systems Department, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt. Reham-abdallah@eru.edu.eg.
Scientific reports
|December 31, 2024
概括
这项研究使用机器学习来预测登革热, Chikungunya 和 Zika 疫情. 一个整体模型实现了Zika的96.80%准确性,显示了传染病预测的高可靠性.
科学领域:
- * 传染病流行病学
- * 公共卫生监督工作
- * 计算生物学 计算生物学
背景情况:
- *传染病对公众健康和经济构成重大威胁.
- *准确的疫情预测对于有效的缓解策略至关重要.
- *气候和社会经济数据是疾病传播的关键指标.
研究的目的:
- *使用先进的机器学习预测登革热,奇孔古尼亚和寨卡疫情.
- * 评估各种机器学习算法在疾病预测中的有效性.
- *利用气候和社会经济数据提高预测准确度.
主要方法:
- * 机器学习算法的应用:随机森林,XGBoost,梯度提升和整体方法.
- *使用分析层次过程 (AHP) 进行特征选择.
- * 整合转移学习以提高预测准确度.
主要成果:
- *整体模型表现出卓越的性能,达到96.80%的准确性和Zika爆发预测的0.9197AUC.
- * 在多个评估指标上观察到一致的高性能.
- *对于奇孔古尼亚,该模型实现了93.31%的准确性,精度 (57%) 和回忆力 (63%) 的平衡.
结论:
- *先进的机器学习,特别是组合方法,为传染病爆发预测提供了可靠的方法.
- *气候和社会经济数据的整合增强了预测能力.
- *这种预测框架可以显著帮助公共卫生倡议在管理和减轻疾病爆发.
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