CONNECTING THE DOTS: LINKING CORONARY DISEASES WITH COVID-19 PATIENTS THROUGH SUPPORT VECTOR MACHINE ALGORITHM
Abstract
The COVID-19 pandemic has left a lasting impact on global health, with a significant portion of survivors experiencing persistent health effects termed as ‘LONG COVID’ or ‘POST COVID-19 SYNDROME’. In this research, we propose a novel approach utilizing Support Vector Machine (SVM) algorithm to analyse patient data and predict the multifaceted nature of post-COVID-19 Syndrome, particularly focusing on the interlinkage of coronary diseases with COVID-19 patients. Our methodology involves collecting and analysing extensive patient data, including pre-conditions and post-conditions of COVID-19, to identify patterns and associations between various health issues. By leveraging the high-dimensional capabilities of SVM, we aim to provide accurate predictions and insights into the long-term health complications of COVID-19 survivors, thereby contributing to a better understanding of this critical area of healthcare. This approach stands out due to its ability to handle nonlinear relationships, noise in data, and large datasets effectively, offering valuable insights for healthcare professionals in managing post-COVID-19 complications.
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