开发一个决策支持系统,用开源软件工具预测流行病的演变.
Myrto Papakonstantinou1, Emmanouil Zoulias1, Parisis Gallos1
1Department of Nursing, National and Kapodistrian University of Athens, Athens, Greece.
Studies in health technology and informatics
|April 9, 2025
概括
这项研究使用机器学习为希腊开发了COVID-19预测系统. 在预测流行病趋势方面,ARIMA模型展示了最高的准确性,提供了可靠的见解.
科学领域:
- 计算流行病学计算流行病学
- 数据科学是数据科学.
- 公共卫生信息学 公共卫生信息学
背景情况:
- 准确的流行病趋势预测对于公共卫生反应至关重要.
- 机器学习为分析复杂的流行病学数据提供了强大的工具.
- 开源软件促进了可访问的预测系统的开发.
研究的目的:
- 开发和评估一个决策支持系统 (DSS),用于预测希腊COVID-19流行趋势.
- 为了比较五种不同的机器学习算法的流行病预测性能.
- 确定最准确的预测模型,为公共卫生战略提供信息.
主要方法:
- 利用开源软件和机器学习算法进行趋势预测.
- 雇佣的COVID-19数据来自OurWorldData.org (2020年初至2022年12月). 工作人员的COVID-19数据来自我们的WorldData.org (2020年初至2022年12月).
- 评估了线性回归,逆向传播 (BP),长期短期记忆 (LSTM),ARIMA和先知模型.
- 使用相关性,平均绝对误差 (MAE),平均平方误差 (MSE) 和根平均平方误差 (RMSE) 评估模型准确性.
主要成果:
- 自行回归集成移动平均线 (ARIMA) 模型被确定为最有效的预测算法.
- 对比分析表明ARIMA模型对希腊COVID-19趋势的优越预测可靠性.
- 开发的DSS显示了准确的流行病预测的巨大潜力.
结论:
- 阿里马模型为预测希腊COVID-19趋势提供了一种可靠的方法.
- 决策支持系统为公共卫生决策提供了有价值的见解.
- 这些发现对机器学习在流行病预测中的更广泛应用有影响.
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