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Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

Author

Listed:
  • Irfan Ullah Khan

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Nida Aslam

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Malak Aljabri

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Sumayh S. Aljameel

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Mariam Moataz Aly Kamaleldin

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Fatima M. Alshamrani

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

  • Sara Mhd. Bachar Chrouf

    (Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia)

Abstract

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

Suggested Citation

  • Irfan Ullah Khan & Nida Aslam & Malak Aljabri & Sumayh S. Aljameel & Mariam Moataz Aly Kamaleldin & Fatima M. Alshamrani & Sara Mhd. Bachar Chrouf, 2021. "Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients," IJERPH, MDPI, vol. 18(12), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6429-:d:574611
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    References listed on IDEAS

    as
    1. Manuel Sánchez-Montañés & Pablo Rodríguez-Belenguer & Antonio J. Serrano-López & Emilio Soria-Olivas & Yasser Alakhdar-Mohmara, 2020. "Machine Learning for Mortality Analysis in Patients with COVID-19," IJERPH, MDPI, vol. 17(22), pages 1-20, November.
    2. Ana Teresa Ferreira & Carlos Fernandes & José Vieira & Filipe Portela, 2021. "Pervasive Intelligent Models to Predict the Outcome of COVID-19 Patients," Future Internet, MDPI, vol. 13(4), pages 1-15, April.
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    Cited by:

    1. Arvind Yadav & Vinod Kumar & Devendra Joshi & Dharmendra Singh Rajput & Haripriya Mishra & Basavaraj S. Paruti, 2023. "Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 12(2), pages 1-15, January.

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