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Genetic‐based adaptive momentum estimation for predicting mortality risk factors for COVID‐19 patients using deep learning by Sally M. Elghamrawy; Aboul Ella Hassanien; Athanasios V. Vasilakos is a Computer Science article available to read on EtoBox.

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## Abstract The mortality risk factors for coronavirus disease (COVID‐19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID‐19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic‐based adaptive momentum estimation (GB‐ADAM) algorithm. The GB‐ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD T Lymphocyte (Count), D‐dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C‐reactive protein (CRP), hypertension, and

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Author

Sally M. Elghamrawy; Aboul Ella Hassanien; Athanasios V. Vasilakos

Publisher

Wiley

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)