Predicting Cardiovascular Complications in Post-COVID-19 Patients Using
  Data-Driven Machine Learning Models
Maitham G. Yousif Hector J. Castro
The COVID-19 pandemic has globally posed numerous health challenges, notably
the emergence of post-COVID-19 cardiovascular complications. This study
addresses this by utilizing data-driven machine learning models to predict such
complications in 352 post-COVID-19 patients from Iraq. Clinical data, including
demographics, comorbidities, lab results, and imaging, were collected and used
to construct predictive models. These models, leveraging various machine
learning algorithms, demonstrated commendable performance in identifying
patients at risk. Early detection through these models promises timely
interventions and improved outcomes. In conclusion, this research underscores
the potential of data-driven machine learning for predicting post-COVID-19
cardiovascular complications, emphasizing the need for continued validation and
research in diverse clinical settings.