Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models
This addresses the health challenge of post-COVID-19 cardiovascular complications for patients and clinicians, but it is incremental as it applies existing machine learning methods to a new clinical dataset.
This study tackled the problem of predicting cardiovascular complications in post-COVID-19 patients using data-driven machine learning models on clinical data from 352 patients in Iraq, achieving commendable performance in identifying at-risk patients.
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.