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Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission
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Author(s): Arvind Yadav (Koneru Lakshmaiah Education Foundation, India), Vinod Kumar (Koneru Lakshmaiah Education Foundation, India), Devendra Joshi (Koneru Lakshmaiah Education Foundation, India), Dharmendra Singh Rajput (Vellore Institute of Technology, India), Haripriya Mishra (Gandhi Institute for Technology, Bhubaneswar, India)and Basavaraj S. Paruti (Ambo University, Ethiopia)
Copyright: 2023
Volume: 12
Issue: 2
Pages: 15
Source title:
International Journal of Reliable and Quality E-Healthcare (IJRQEH)
Editor(s)-in-Chief: Anastasius Moumtzoglou (Hellenic Society for Quality & Safety in Healthcare and P. & A. Kyriakou Children's Hospital, Greece)
DOI: 10.4018/IJRQEH.320480
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Abstract
COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.
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