The Predictive Analysis of the COVID-19 Prognosis Using Machine Learning and Artificial Intelligence Techniques

Overview

The COVID-19 pandemic has spread rapidly around the world, and it is currently one of the most leading causes of death and a major health disaster in the world. Due to the rapid spread and contagious nature of the COVID-19, medical professionals need to make informed decisions on how to diagnose and treat the disease effectively. The aim of the study is to design a predictive model based on machine learning and artificial intelligence techniques to predict the prognosis of COVID-19 and help medical professionals to choose the right course of treatment for their patients. In this study, data from Immune Epitope Database (IEDB), a freely available resource funded by National Institute of Allergy and Infectious disease, was used. This study provides a comparative analysis of different machine learning techniques, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest, in predicting the prognosis and assisting healthcare providers with the prognosis of the COVID-19 patients. The results show that the Random Forest method performed better in predicting the diagnosis and prognosis of the disease. Early diagnosis will help healthcare professionals to make informed decisions at an early stage and will aid in understanding the disease progression that can help in treatment of the disease and to select the line of antibiotics that need to be used for treatment.

Competition history

• JSHS 2022 Category not listed

Resources

• National JSHS abstract book ↗

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