Iftikhar Hasnain, Khan Murad, Khan Mohammed Saad, Khan Mehak
Department of Mathematics, City University of Science and Information Technology, Peshawar 25000, Pakistan.
Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan.
Diagnostics (Basel). 2023 May 31;13(11):1923. doi: 10.3390/diagnostics13111923.
In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics of infected and uninfected people worldwide contribute to the growing number of publicly available datasets that can be used to predict early-stage confirmed cases of monkeypox through machine-learning models. Thus, this paper proposes a novel filtering and combination technique for accurate short-term forecasts of infected monkeypox cases. To this end, we first filter the original time series of the cumulative confirmed cases into two new subseries: the long-term trend series and residual series, using the two proposed and one benchmark filter. Then, we predict the filtered subseries using five standard machine learning models and all their possible combination models. Hence, we combine individual forecasting models directly to obtain a final forecast for newly infected cases one day ahead. Four mean errors and a statistical test are performed to verify the proposed methodology's performance. The experimental results show the efficiency and accuracy of the proposed forecasting methodology. To prove the superiority of the proposed approach, four different time series and five different machine learning models were included as benchmarks. The results of this comparison confirmed the dominance of the proposed method. Finally, based on the best combination model, we achieved a forecast of fourteen days (two weeks). This can help to understand the spread and lead to an understanding of the risk, which can be utilized to prevent further spread and enable timely and effective treatment.
在现代世界中，人工智能、机器学习和大数据等新技术对于支持医疗监测系统至关重要，尤其是在监测猴痘确诊病例方面。全球感染和未感染人群的统计数据促使公开可用数据集数量不断增加，这些数据集可用于通过机器学习模型预测猴痘早期确诊病例。因此，本文提出了一种新颖的过滤和组合技术，用于对感染的猴痘病例进行准确的短期预测。为此，我们首先使用两种提出的滤波器和一种基准滤波器，将累计确诊病例的原始时间序列过滤为两个新的子序列：长期趋势序列和残差序列。然后，我们使用五种标准机器学习模型及其所有可能的组合模型对过滤后的子序列进行预测。因此，我们直接将各个预测模型组合起来，以获得提前一天的新感染病例的最终预测。进行了四个平均误差和一项统计检验，以验证所提出方法的性能。实验结果表明了所提出的预测方法的有效性和准确性。为了证明所提出方法的优越性，纳入了四个不同的时间序列和五个不同的机器学习模型作为基准。这种比较的结果证实了所提出方法的优势。最后，基于最佳组合模型，我们实现了为期十四天（两周）的预测。这有助于了解传播情况并进而了解风险，可利用这些信息防止进一步传播并实现及时有效的治疗。
