COVID-19 Prediction Using Traditional and Deep Learning Models | ||
| Benha Journal of Applied Sciences | ||
| Volume 9, Issue 5, May 2024, Pages 295-301 PDF (847.84 K) | ||
| Document Type: Original Research Papers | ||
| DOI: 10.21608/bjas.2025.346619.1560 | ||
| Authors | ||
| Mai Rashid* 1; Hamada Nayel2; ahmed Taha3 | ||
| 1Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt | ||
| 2Department of Computer Science, faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt | ||
| 3computer science | ||
| Abstract | ||
| In response to the pressing challenges posed by the COVID-19 pandemic, this research endeavors to revolutionize disease classification through an innovative fusion of data analytics and advanced machine learning methodologies. The proposed study meticulously employs a dataset enriched with key physiological parameters namely, oxygen levels, pulse rates, and temperatures leveraging a systematic approach to dataset analysis, exploratory data analysis, and preprocessing. The research addresses a critical problem: the accurate and timely classification of COVID-19 cases. The developed methodology encompasses a diverse array of models, from traditional machine learning techniques to sophisticated deep learning architectures, ensuring a comprehensive evaluation. Through rigorous model selection, hyperparameter tuning, and performance analysis, we unravel actionable insights. The achieved results of the proposed model are very competitive with state-of-the-art models. This research not only contributes to the scientific understanding of COVID-19 classification but also lays the foundation for deploying effective machine learning tools in real-world scenarios for infectious disease management. | ||
| Keywords | ||
| COVID-19 Prediction; ML; DL; Model Selection; Hyperparameter Tuning | ||
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