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ORIGINAL RESEARCH article

Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1427239
This article is part of the Research Topic Cluster-based Intelligent Recommendation System for Hybrid Healthcare Units View all 16 articles

Deep Learning Infused SIRVD Model for COVID-19 Prediction: XGBoost-SIRVD-LSTM Approach

Provisionally accepted
Hisham Alkhalefah Hisham Alkhalefah 1Preethi D. Preethi D. 2Neelu Khare Neelu Khare 3MUSTUFA H. ABIDI MUSTUFA H. ABIDI 1*Usama Umer Usama Umer 1
  • 1 King Saud University, Riyadh, Saudi Arabia
  • 2 SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
  • 3 VIT University, Vellore, Tamil Nadu, India

The final, formatted version of the article will be published soon.

    The global impact of the ongoing COVID-19 pandemic, while somewhat contained, remains a critical challenge that has tested the resilience of humanity. Accurate and timely prediction of COVID-19 transmission dynamics and future trends is essential for informed decision-making in public health. Deep learning and mathematical models have emerged as promising tools, yet concerns regarding accuracy persist. This research suggests a novel model for forecasting the COVID-19's future trajectory. The model combines the benefits of machine learning models and mathematical models. The SIRVD model, a mathematical based model that depicts the reach of the infection via population, serves as basis for the proposed model. A deep prediction model for COVID-19 using XGBoost-SIRVD-LSTM is presented. The suggested approach combines SIRVD (Susceptible-Infected-Recovered-Vaccinated-Deceased), and a deep learning model, which includes LSTM (Long Short-Term Memory) and other prediction models, including feature selection using XGBoost method. The model keeps track of changes in each group's membership over time. To increase the SIRVD model's accuracy, machine learning is applied. The key properties for forecasting the spread of the infection are found using a method called feature selection. Then, in order to learn from these features and create predictions, a model involving deep learning is applied. The performance of the model proposed was assessed with prediction metrics such as R 2 , RMSE (root mean square error), MAPE (mean absolute percentage error), and NRMSE (normalized root mean square error). The results are also validated to those of other prediction models. The empirical results show that the suggested model outperforms similar models. Findings suggest its potential as a valuable tool for pandemic management and public health decision-making.

    Keywords: deep learning, XGBoost (Extreme Gradient Boosting), SIRVD (Susceptible-Infected-Recovered-Vaccination-Deceased), LSTM (Long Short-Term Memory), Feature Selection, COVID-19, prediction

    Received: 03 May 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Alkhalefah, D., Khare, ABIDI and Umer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: MUSTUFA H. ABIDI, King Saud University, Riyadh, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.