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随机森林回归用于预测土耳其的Covid-19日常病例和死亡人数
1Department of Electrical and Electronics Engineering, Haliç University, Istanbul, Turkey.
Heliyon
|February 19, 2024
概括
疫情防控需要对医院容量和死亡率进行有效的规划. 这项研究使用机器学习准确预测土耳其的每日COVID-19病例和死亡,为未来的健康危机提供了宝贵的见解.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 流行病给医疗保健系统带来压力,导致医院病床和重症监护病房的严重短缺.
- 有效的规划对于管理健康危机期间的患者流动和死亡率至关重要.
- 从COVID-19大流行中吸取的经验教训可以为未来的事件做好准备.
研究的目的:
- 分析土耳其每日COVID-19病例和死亡的统计属性.
- 开发和比较机器学习模型,用于预测每日病例和死亡.
- 用各种准确性和错误指标来评估模型性能.
主要方法:
- 土耳其每日COVID-19病例和死亡数据的统计分析.
- 应用随机森林回归来进行预测.
- 与其他七个机器学习模型进行比较:包装,AdaBoost,梯度提升,XGBoost,决策树,LSTM和ARIMA.
主要成果:
- 土耳其的每日COVID-19病例表现出非静止的特征.
- 随机森林回归实现了92.30%的准确性,每日病例预测的R2得分为0.9893.
- 随机森林回归实现了91.39%的准确性和R2得分为0.9834的每日死亡预测.
- 包装回归器显示了相似的性能,但具有运行到运行的变化.
结论:
- 机器学习模型,特别是随机森林回归,可以有效地预测COVID-19的每日病例和死亡.
- 准确的预测模型对于疫情准备和资源分配至关重要.
- 该研究强调了在公共卫生紧急情况中强大和一致的预测模型的重要性.
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