Coronavirus Disease 2019 (COVID-19): Prediction Strategy Using Sequential Deep Learning Model

Amit Shaha Surja, Md. Shahid Iqbal, Md. Omor Faruk
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Abstract

Since the globe has faced extreme difficulties with COVID-19, Artificial Intelligence appeared to help to cope with this epidemic in an innumerable number of ways. Motivated by this, in this article, a robust prediction model called COVID-SDL has been proposed using Sequential Deep Learning (SDL) for predicting the total positive cases per day. In order to evaluate the performance of COVID-SDL, data samples used in the model have been collected from Italy’s COVID-19 situation reports. Besides this, the dataset has gone through the processes of cleaning, filtering, formatting and visualization. COVID-SDL utilizes the correlation information among the features that have strengthened the prediction capability. Also, the exploratory survey showed that 5 most salient features (Home Confinement, Deaths, Recovered, Current Positive Cases and Tests Performed) results better which is obtained from the mentioned dataset primarily composed of 17 features. In addition, to assist the prediction ability of COVID-SDL, ReLu (Rectified Linear Unit) activation function has been used which enhanced the robustness of the model. With a view to making the predictions highly accurate, Adam optimizer has been adopted which works by reducing the cost function and making further updates of the weights. Moreover, COVID-SDL has successfully obtained accuracy parameters such as MAE- 0.00037316, MSE- 0.00000018, RMSE- 0.00043476 and R2 Score- 0.99999 with providing the best fit curve of predicted data which covers 99.999% of the actual data. Furthermore, to prove the robustness of the COVID-SDL, a comparative test among the adaptive and non-adaptive optimizers has also been performed.
2019冠状病毒病(COVID-19):使用顺序深度学习模型的预测策略
由于全球面临着COVID-19的极端困难,人工智能似乎以无数的方式帮助应对这一流行病。受此启发,本文提出了一种名为COVID-SDL的鲁棒预测模型,使用顺序深度学习(SDL)来预测每天的阳性病例总数。为了评估COVID-SDL的性能,模型中使用的数据样本来自意大利的COVID-19情况报告。除此之外,数据集还经历了清理、过滤、格式化和可视化的过程。COVID-SDL利用了特征之间的相关信息,增强了预测能力。此外,探索性调查显示,从主要由17个特征组成的上述数据集中获得的5个最显著特征(家庭隔离、死亡、康复、当前阳性病例和进行的测试)结果更好。此外,为了辅助COVID-SDL的预测能力,我们使用了ReLu (Rectified Linear Unit,整流线性单元)激活函数,增强了模型的鲁棒性。为了使预测高度准确,我们采用了亚当优化器,它通过减少成本函数和进一步更新权重来工作。此外,COVID-SDL成功获得了MAE- 0.00037316、MSE- 0.00000018、RMSE- 0.00043476、R2 Score- 0.99999等精度参数,并提供了预测数据的最佳拟合曲线,覆盖了99.999%的实际数据。此外,为了证明COVID-SDL的鲁棒性,还对自适应优化器和非自适应优化器进行了比较测试。
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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