Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models

TL;DR

This study develops machine learning models to predict cardiovascular complications in post-COVID-19 patients, aiming for early detection and improved clinical outcomes.

Contribution

It introduces a data-driven approach using clinical data and machine learning algorithms to predict post-COVID cardiovascular issues, which is novel in this context.

Findings

Models achieved high predictive accuracy.

Early detection can enable timely interventions.

Demonstrated effectiveness in a clinical setting.

Abstract

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.