SVM has been used in several studies in bioinformatics concerning disease classification. Currently, the world is experiencing a pandemic called COVID-19 which is a contagious disease that can be transferred through droplets in the air from the infected person. Individuals who carry this virus demand to be diagnosed promptly to prevent the virus from spreading which can cause danger to human lives, but COVID-19 testing kits were not easily accessible, and laboratory tests cannot be done right away. In line with this, the authors utilized Support Vector Machine supervised machine learning algorithm in building a model to analyze and predict the presence of COVID-19 in a person based on symptoms experienced by the person. Hyperparameters such as degree, cost, gamma, and kernels including Linear, Radial, Polynomial, and Sigmoid were tuned through R Studio to attain the best possible performance of the model. The model was evaluated using 10 - fold cross validation and results show that polynomial kernel with optimized hyperparameters yielded the best accuracy of 98.02%. © 2021 ACM.
Support Vector Machine Modelling for COVID-19 Prediction based on Symptoms using R Programming Language