Development Of Machine Learning Techniques To Differentiate COVID-19 Indications From Serious Diseases

Document Type : Primary Research paper

Authors

1 Associate Professor, Department of Computer Science and Engineering, GIET University, Gunupur, Odisha 765022.

2 Assistant Professor, Department of Computer Science and Engineering, University Institute of Technology, RGPV Bhopal Madhya Pradesh-462033.

3 Professor, Department of Computer Science and Engineering, Dr.Samuel George Institute of Engineering & Technology, Markapur, Prakasam Dt, Andhra Pradesh, 523316.

4 Assistant Professor, Department of Computer Science and Engineering, Avanthi Institute of Engineering and Technology, Gunthapally, Abdullahpurmet Mandal-501512, Hyderabad, Telangana.

5 Assistant Professor, Department of Information Technology, Shri Ramdeobaba College of Engineering and Management, Gittikhadan, Katol Road, Nagpur, Maharashtra, India-440014

6 Assistant Professor, Department of Computer Science, Anna Adarsh College for Women, A1,II street,9th Main Road,Anna Nagar,Chennai -600040.

Abstract

Considering the identical signs of both covid-19 and influenza, most individuals are unable to distinguish between the two, which can result in a person's death. To control the death rate, several approaches are needed to categories the signs of covid-19 and other diseases. Severe sickness is more likely to hit the elderly and individuals with underlying medical conditions and diseases lung diseases and cancer. In the context of the present outbreak, identification of these diseases is limited to a few clinical studies like RT-PCR and CT-Scan of lung pictures to detect the covid-19. We will develop a method to solve the present issues experienced by people in the outbreak condition, as these examinations take a long time and are highly expensive. Researchers discovered that image processing, data mining, artificial intelligence, and pattern recognition are widely utilized approaches for solving these problems after doing a research study.

Keywords

Int. J. of Aquatic Science
Volume 12, Issue 2 - Serial Number 2
June 2021
Pages 2062-2067

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