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The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posedsignificant health challenges worldwide. While respiratory symptoms have beenthe primary focus, emerging evidence has highlighted the impact of COVID-19 onvarious organ systems, including the gastrointestinal (GI) tract. This study,based on data from 913 post-COVID-19 patients in Iraq collected during 2022 and2023, investigates the prevalence and patterns of GI symptoms in individualsrecovering from COVID-19 and leverages machine learning algorithms to identifypredictive factors for these symptoms. The research findings reveal that anotable percentage of post-COVID-19 patients experience GI symptoms duringtheir recovery phase. Diarrhea emerged as the most frequently reported symptom,followed by abdominal pain and nausea. Machine learning analysis uncoveredsignificant predictive factors for GI symptoms, including age, gender, diseaseseverity, comorbidities, and the duration of COVID-19 illness. These findingsunderscore the importance of monitoring and addressing GI symptoms inpost-COVID-19 care, with machine learning offering valuable tools for earlyidentification and personalized intervention. This study contributes to theunderstanding of the long-term consequences of COVID-19 on GI health andemphasizes the potential benefits of utilizing machine learning-driven analysisin predicting and managing these symptoms. Further research is warranted todelve into the mechanisms underlying GI symptoms in COVID-19 survivors and todevelop targeted interventions for symptom management. Keywords: COVID-19,gastrointestinal symptoms, machine learning, predictive factors, post-COVID-19care, long COVID.

Machine Learning-driven Analysis of Gastrointestinal Symptoms in Post-COVID-19 Patients