  The COVID-19 pandemic has left a lasting impact on individuals, with many
experiencing persistent symptoms, including inflammation, in the post-acute
phase of the disease. Detecting and monitoring these inflammatory biomarkers is
critical for timely intervention and improved patient outcomes. This study
employs machine learning techniques to automate the identification of
persistent inflammatory biomarkers in 290 post-COVID-19 patients, based on
medical data collected from hospitals in Iraq. The data encompassed a wide
array of clinical parameters, such as C-reactive protein and interleukin-6
levels, patient demographics, comorbidities, and treatment histories. Rigorous
data preprocessing and feature selection processes were implemented to optimize
the dataset for machine learning analysis. Various machine learning algorithms,
including logistic regression, random forests, support vector machines, and
gradient boosting, were deployed to construct predictive models. These models
exhibited promising results, showcasing high accuracy and precision in the
identification of patients with persistent inflammation. The findings of this
study underscore the potential of machine learning in automating the detection
of persistent inflammatory biomarkers in post-COVID-19 patients. These models
can serve as valuable tools for healthcare providers, facilitating early
diagnosis and personalized treatment strategies for individuals at risk of
persistent inflammation, ultimately contributing to improved post-acute
COVID-19 care and patient well-being. Keywords: COVID-19, post-COVID-19,
inflammation, biomarkers, machine learning, early detection.
