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The research titled "Early Detection of Post-COVID-19 Fatigue Syndrome usingDeep Learning Models" addresses a pressing concern arising from the COVID-19pandemic. Post-COVID-19 Fatigue Syndrome (PCFS) has become a significant healthissue affecting individuals who have recovered from COVID-19 infection. Thisstudy harnesses a robust dataset comprising 940 patients from diverse agegroups, whose medical records were collected from various hospitals in Iraqover the years 2022, 2022, and 2023. The primary objective of this research isto develop and evaluate deep learning models for the early detection of PCFS.Leveraging the power of deep learning, these models are trained on acomprehensive set of clinical and demographic features extracted from thedataset. The goal is to enable timely identification of PCFS symptoms inpost-COVID-19 patients, which can lead to more effective interventions andimproved patient outcomes. The study's findings underscore the potential ofdeep learning in healthcare, particularly in the context of COVID-19 recovery.Early detection of PCFS can aid healthcare professionals in providing timelycare and support to affected individuals, potentially reducing the long-termimpact of this syndrome on their quality of life. This research contributes tothe growing body of knowledge surrounding COVID-19-related health complicationsand highlights the importance of leveraging advanced machine learningtechniques for early diagnosis and intervention. Keywords: Early Detection,Post-COVID-19 Fatigue Syndrome, Deep Learning Models, Healthcare, COVID-19Recovery, Medical Data Analysis, Machine Learning, Health Interventions.

Early Detection of Post-COVID-19 Fatigue Syndrome Using Deep Learning Models