Abstract
Coronavirus is the new pandemic hitting all over the world. Patients all over the world are facing different symptoms. Most of the patients with severe symptoms die especially the elderly. In this chapter, three machine learning techniques have been chosen and tested to predict the patient’s recovery of Coronavirus disease. The support vector machine has been tested on the given data with a mean absolute error of 0.2155. The Epidemiological data set is prepared by researchers from many health reports of real-time cases to represent the different attributes that contribute as the main factors for recovery prediction. Deep analysis with other machine learning algorithms including artificial neural networks and regression models has been tested and compared with the SVM results. The experimental results show that most of the patients who could not recover had a fever, cough, general fatigue, and most probably malaise.
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Salama, A., Darwsih, A., Hassanien, A.E. (2021). Artificial Intelligence Approach to Predict the COVID-19 Patient’s Recovery. In: Hassanien, A.E., Darwish, A. (eds) Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches. Studies in Systems, Decision and Control, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-63307-3_8
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