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arXiv · 2310.03035

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

Fadhil G. Al-Amran·Salman Rawaf·Maitham G. Yousif
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Abstract

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

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Fadhil G. Al-Amran, Salman Rawaf, Maitham G. Yousif. 2023-09-26. Early Detection of Post-COVID-19 Fatigue Syndrome Using Deep Learning Models. https://arxiv.org/abs/2310.03035

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