  The COVID-19 pandemic has left a lasting impact on global healthcare systems,
with increasing evidence of pulmonary fibrosis emerging as a post-infection
complication. This study presents a comprehensive analysis of pulmonary
fibrosis patterns in post-COVID-19 patients from South and Central Iraq,
employing advanced machine learning algorithms. Data were collected from 390
patients, and their medical records were systematically analyzed. Our findings
reveal distinct patterns of pulmonary fibrosis in this cohort, shedding light
on the heterogeneous nature of post-COVID-19 lung complications. Machine
learning models demonstrated robust predictive capabilities, offering valuable
insights into the characterization of fibrotic changes. The identification of
specific patterns contributes to early diagnosis and personalized treatment
strategies for affected individuals. This research underscores the importance
of data-driven approaches in understanding post-COVID-19 complications,
particularly in regions with unique demographic and healthcare characteristics.
It emphasizes the potential for machine learning to enhance clinical
decision-making and improve patient care in the aftermath of the pandemic.
Further investigations are warranted to validate these findings and explore
additional factors influencing pulmonary fibrosis in post-COVID-19 patients.
