Chandran Venkatesan, Karthick A and Anto Merline M, Department of ECE, KPR Institute of Engineering and Technology, India
Sumithra M G, Department of Bio Medical Engineering, KPR Institute of Engineering and Technology, India
Elakkiya Balan, Department of Electronics and Communication Engineering, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India,
Jayarajan V, Associate Consultant, ATOS Syntel, India
Online First : 30 December 2020
Publisher Name : IJAICT India Publications, India.
Print ISBN : 978-81-950008-0-7
Online ISBN : 978-81-950008-1-4
Page :447-452
Abstract
In this current scenario, covid pandemic breaks analysis is becoming popular among the researchers. The various data sources from the different countries analyzed to predict the possibility of coronavirus transition from one person to another person. The datasets are not providing more information about the causes of the corona. Many authors provided the solution by using chest X-ray and CT images to predict the corona. In this paper, the covid pandemic transition process from one person to another person was classified using traditional machine learning algorithms. The input labels are encoded and transformed, utilizing the label encoder technique. The XG boost algorithm was outperformed all the other algorithms with overall accuracy and F1-measure of 99%. The Na誰ve Bayes algorithm provides 100% accuracy, precision, recall, and F1-Score due to its improved ability to handle lower datasets.
Keywords
Pandemic, Machine Learning (ML), Corona, XG boost, Na誰ve Bayes.
Cite this article
Chandran Venkatesan, Karthick A, Anto Merline M, Sumithra M G, Elakkiya Balan and Jayarajan V, “IOT Based Remote Livelihood Monitoring For Elderly People Care AssessmentA Prediction of Corona Disease Transmission Using A Traditional Machine Learning Approach”, Innovations in Information and Communication Technology, pp.447-452, December 2020.
Copyright
© 2020 Chandran Venkatesan, Karthick A, Anto Merline M, Sumithra M G, Elakkiya Balan and Jayarajan V. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
