Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques

IF 0.9 Q3 MATHEMATICS, APPLIED
Tanzina Akter, Md. Farhad Hossain, Mohammad Safi Ullah, Rabeya Akter
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Abstract

Predicting mortality in COVID-19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India, Brazil, Russia, France, China, and Bangladesh. Data were gathered between December 31, 2019, when COVID-19 began, and March 31, 2021. The study used 457 observations with 4 variables: daily confirmed cases, daily deaths, daily mortality rate, and date. To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. The autoregressive integrated moving average (ARIMA) model with the lowest AIC value for each nation is found through time series analysis. By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. Additionally, SVM analyzes every country and calculates its R2 and root-mean-square error (RMSE). The lowest RMSE value is used to compare all of the time series and machine learning models. According to the comparison table, SVM provides a more accurate model to predict the mortality rate of the seven countries, with the lowest RMSE value. During the study period, mortality rates increased in Brazil and Russia and decreased in the United States, India, France, China, and Bangladesh, according to the comparison value of RMSE in this study. Furthermore, this paper shows that SVM outperforms all other models in terms of performance. According to the author’s analysis of the data, SVM is a machine learning technique that can be used to accurately predict mortality in a pandemic scenario.

Abstract Image

利用时间序列和机器学习技术预测 COVID-19 的死亡率
预测 COVID-19 的死亡率是当前最重要、最困难的任务之一。本研究比较了时间序列和机器学习方法,包括支持向量机(SVM)和神经网络(NN),以预测美国、印度、巴西、俄罗斯、法国、中国和孟加拉国这七个国家的死亡率。数据收集时间为 2019 年 12 月 31 日 COVID-19 开始至 2021 年 3 月 31 日。研究使用了 457 个观测值,包含 4 个变量:每日确诊病例、每日死亡病例、每日死亡率和日期。为了预测所选 7 个国家的死亡率,我们使用时间序列分析和机器学习技术对数据进行了分析。对各种模型进行了比较,以获得更准确的死亡率预测。通过时间序列分析,为每个国家找到了 AIC 值最低的自回归综合移动平均(ARIMA)模型。通过增加隐层和应用机器学习技术,为每个国家选择 NN 模型,并通过确定误差值最小的模型来确定最佳模型。此外,SVM 对每个国家进行分析,并计算其 R2 和均方根误差 (RMSE)。最小 RMSE 值用于比较所有时间序列和机器学习模型。根据比较表,SVM 提供了一个更准确的模型来预测七个国家的死亡率,其 RMSE 值最低。根据本研究的 RMSE 比较值,在研究期间,巴西和俄罗斯的死亡率上升,而美国、印度、法国、中国和孟加拉国的死亡率下降。此外,本文还显示 SVM 在性能方面优于所有其他模型。根据作者对数据的分析,SVM 是一种可用于准确预测大流行情况下死亡率的机器学习技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.20
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