Machine Learning-driven Analysis of Gastrointestinal Symptoms in
  Post-COVID-19 Patients
Maitham G. Yousif Fadhil G. Al-Amran Salman Rawaf Mohammad Abdulla Grmt
The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed
significant health challenges worldwide. While respiratory symptoms have been
the primary focus, emerging evidence has highlighted the impact of COVID-19 on
various organ systems, including the gastrointestinal (GI) tract. This study,
based on data from 913 post-COVID-19 patients in Iraq collected during 2022 and
2023, investigates the prevalence and patterns of GI symptoms in individuals
recovering from COVID-19 and leverages machine learning algorithms to identify
predictive factors for these symptoms. The research findings reveal that a
notable percentage of post-COVID-19 patients experience GI symptoms during
their recovery phase. Diarrhea emerged as the most frequently reported symptom,
followed by abdominal pain and nausea. Machine learning analysis uncovered
significant predictive factors for GI symptoms, including age, gender, disease
severity, comorbidities, and the duration of COVID-19 illness. These findings
underscore the importance of monitoring and addressing GI symptoms in
post-COVID-19 care, with machine learning offering valuable tools for early
identification and personalized intervention. This study contributes to the
understanding of the long-term consequences of COVID-19 on GI health and
emphasizes the potential benefits of utilizing machine learning-driven analysis
in predicting and managing these symptoms. Further research is warranted to
delve into the mechanisms underlying GI symptoms in COVID-19 survivors and to
develop targeted interventions for symptom management. Keywords: COVID-19,
gastrointestinal symptoms, machine learning, predictive factors, post-COVID-19
care, long COVID.