2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 2 , PP: 264-278, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ali Khraisat 1 * , Mohd Khanapi Abd Ghani 2
Doi: https://doi.org/10.54216/FPA.170220
This review provides an in-depth exploration of machine learning (ML) applications in healthcare, focusing specifically on predictive models for COVID-19 transmission among vaccinated individuals. It underscores the pivotal role of ML in disease forecasting and prognosis, showcasing its potential to enhance healthcare outcomes in pandemic contexts. Key challenges of COVID-19, such as the high transmission rate of asymptomatic carriers and the effectiveness of containment strategies, are analyzed to highlight areas where ML can offer significant advantages. The study aims to develop an advanced forecasting model for COVID-19 transmission using diverse supervised ML regression techniques, including linear regression, LASSO, support vector machine, and exponential smoothing, applied to an extensive COVID-19 patient dataset. The insights generated from this review support efforts to combat COVID-19 and improve public health strategies, demonstrating ML's vital contribution to pandemic management and healthcare resilience.
Machine learning , healthcare, COVID-19 , Predictive models , Disease forecasting , Disease prognosis , Vaccinated individuals , COVID-19 transmission
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