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Predicting the recovery of COVID-19 patients using recurrent neural network and Markov chain
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Mehrdad Fadaei PellehShahi    , Sohrab Kordrostami , Amir Hossein Refahi Sheikhani , Marzieh Faridi Masouleh , Soheil Shokri |
| Department of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran , kordrostami@liau.ac.ir |
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Abstract: (475 Views) |
In this paper, a new method is presented using a combination of deep learning method, specifically recursive neural network, and Markov chain. The aim is to obtain more realistic results with lower cost in predicting COVID-19 patients. For this purpose, the BestFirst algorithm is used for the search section, and the Cfssubseteval algorithm is implemented for evaluating the features in the data preprocessing section. The proposed method is simulated using the real data of COVID-19 patients who were hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020. The obtained results were compared with three valid advanced methods. The results showed that the proposed method significantly reduces the amount of memory resource usage and CPU usage time compared to similar methods, and at the same time, the accuracy also increases significantly.
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| Keywords: Prediction, Covid-19, Recovery, Markov Chain, Recurrent Neural Network |
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Full-Text [PDF 553 kb]
(1167 Downloads)
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Type of Study: Original |
Subject:
Mathematical Modeling and Applications of OR
Received: 2023/07/31 | Accepted: 2022/12/8 | Published: 2022/12/8
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