Ismail, Leila and Materwala, Huned and Al Hammadi, Yousef and Firouzi, Farshad and Khan, Gulfaraz and Azzuhri, Saaidal Razalli Bin (2022) Automated artificial intelligence-enabled proactive preparedness real-time system for accurate prediction of COVID-19 infections- Performance evaluation. Frontiers in Medicine, 9. ISSN 2296-858X, DOI https://doi.org/10.3389/fmed.2022.871885.
Full text not available from this repository.Abstract
COVID-19 is a contagious disease that has infected over half a billion people worldwide. Due to the rapid spread of the virus, countries are facing challenges to cope with the infection growth. In particular, healthcare organizations face difficulties efficiently provisioning medical staff, equipment, hospital beds, and quarantine centers. Machine and deep learning models have been used to predict infections, but the selection of the model is challenging for a data analyst. This paper proposes an automated Artificial Intelligence-enabled proactive preparedness real-time system that selects a learning model based on the temporal distribution of the evolution of infection. The proposed system integrates a novel methodology in determining the suitable learning model, producing an accurate forecasting algorithm with no human intervention. Numerical experiments and comparative analysis were carried out between our proposed and state-of-the-art approaches. The results show that the proposed system predicts infections with 72.1% less Mean Absolute Percentage Error (MAPE) and 65.2% lower Root Mean Square Error (RMSE) on average than state-of-the-art approaches.
| Item Type: | Article |
|---|---|
| Funders: | National Water and Energy Center of the United Arab Emirates University [31R215] |
| Uncontrolled Keywords: | Automated artificial intelligence (Auto-AI); Coronavirus; COVID-19 infection prediction; Deep learning; Healthcare; Machine learning; Performance evaluation; Time series |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 19 Sep 2023 04:29 |
| Last Modified: | 19 Sep 2023 04:29 |
| URI: | http://eprints.um.edu.my/id/eprint/41333 |
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