PREDICTING LONG-TERM COVID-19 SYMPTOMS USING MACHINE LEARNING: A CASE STUDY IN KURDISTAN REGION OF IRAQ - MUSTAFA, AVEEN KAKAMEN; HAMARASH, IBRAHIM ISMAEL (Journal of Duhok University, 2023)
The COVID-19 pandemic has introduced substantial challenges to individuals, communities, and healthcare systems worldwide. While initial responses primarily addressed the acute impact of the virus, emerging evidence highlights a noteworthy portion of individuals grappling with persistent symptoms even after recuperating from the acute phase. This research delves into the domain of algorithms and their application to the context of COVID-19. Specifically, we employ Machine Learning (ML) techniques to formulate a robust model for assessing the likelihood of enduring long-term COVID-19 symptoms among individuals in the recovery phase. Our investigation revolves around a comprehensive dataset drawn from 3,500 patients residing in the Kurdistan Region of Iraq, all of whom had previously contracted COVID-19. Employing a combination of hospital records and direct/mobile interviews, we systematically capture information pertaining to six prevalent long-term symptoms. Rigorous preprocessing techniques are then applied to the collected data, ensuring standardization and mitigating any inherent inconsistencies or biases. To achieve our objective, we harness the capabilities of the TensorFlow and Keras libraries, leveraging a deep learning algorithm. This algorithm plays a pivotal role in predicting the probability of sustained COVID-19 symptoms among recovered patients. This endeavor demonstrates the potential of deep learning, especially when harnessed within a well-structured dataset and coupled with adept preprocessing methodologies. Consequently, our findings underscore the viability of utilizing deep learning algorithms as potent tools for forecasting the propensity of long-term symptom manifestation in individuals previously diagnosed with COVID-19.