Rapid Thrombogenesis Prediction in Covid-19 Patients Using Machine Learning

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims to decrease the waiting time for clinicians to receive this information, leading to quicker treatment plans and improved patient outcomes. And we trained and tested several ML algorithms and found the optimal training data size while maintaining high accuracy. The dataset for this study was collected from previous studies and was pre-processed to ensure that the data was reliable and accurate. The proposed system’s main contribution is the ability to quickly predict thrombogenesis in Covid-19 patients using ML models, which can help in preventive medicine by detecting serious diseases in advance.