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Predicting the recovery of COVID-19 patients using recursive deep learning
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Mehrdad Fadaei PellehShahi    , Sohrab Kordrostami , Amir Hosein Refahi Sheikhani * , Marzieh Faridi Masouleh , Soheil Shokri |
| Lahijan Branch, Islamic Azad University , ah_refahi@liau.ac.ir |
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Abstract: (3795 Views) |
| In this study, an alternative method is proposed based on recursive deep learning with limited steps and prepossessing, in which the data is divided into A unit classes in order to change a long short term memory and solve the existing challenges. The goal is to obtain predictive results that are closer to real world in COVID-19 patients. To achieve this goal, four existing challenges including the heterogeneous data, the imbalanced data distribution in predicted classes, the low allocation rate of data to a class and the existence of many features in a process have been resolved. The proposed method is simulated using the real data of COVID-19 patients hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020, which has led to recovery or death. The obtained results are compared against three valid advanced methods, and are showed that the amount of memory resources usage and CPU usage time are slightly increased compared to similar methods and the accuracy is increased by an average of 12%. |
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| Keywords: Long Short Term Memory, Recurrent Deep Learning, Prediction, COVID-19, Neural Network |
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Full-Text [PDF 639 kb]
(4698 Downloads)
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Type of Study: Original |
Subject:
Mathematical Modeling and Applications of OR
Received: 2021/12/5 | Accepted: 2021/06/12 | Published: 2021/06/12
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