Corona Virus Infection Probability Classification using Support Vector Machine

  • Saumendra Kumar Mohapatra, Mohan Debarchan Mohanty, and Mihir Narayan Mohanty

Abstract

Due to the unexpected outbreak of COVID-19 disease, the world is facing a major epidemic in current
days. The infection as well as the death rate is growing rapidly in every country. The world
economicstatus is also decreasing due to this disaster. It is more essential to detect the infected people
at an early stage to make a break in spreading of virus. Machine learning techniques will be very
useful for this purpose due to its automatic data analysis and classification ability. In the proposed
work, authors have classified samples having chance of infection. A set of randomly generated data is
considered for the classification purpose. The dataset contains 2889 samples with five types of
COVID symptoms. By analysing the body temperature, age, body pain, runny nose status, and
breathing problem a support vector machine (SVM) classifier is classifying the people having
infection probability. The performance of SVM classifier is measured in terms of accuracy, precision,
and recall. From the obtained result it is observed that SVM is providing better result with linear
kernel.

Published
2020-04-13
How to Cite
Saumendra Kumar Mohapatra, Mohan Debarchan Mohanty, and Mihir Narayan Mohanty. (2020). Corona Virus Infection Probability Classification using Support Vector Machine. International Journal of Advanced Science and Technology, 29(8s), 3093-3098. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/16375