Analysis and Comparison for Innovative Prediction Technique of COVID-19 using Logistic Regression algorithm over Support Vector Machine Algorithm with Improved Accuracy.
Garudadri Venkata, Sree Charan, Neelam Sanjeev Kumar, Rajesh Adhinarayanan, Aravindh Ramakrishnan, Gopal Kaliyaperumal, Melvinvctor De Poures, Rajesh Kumar Babu, Damodharan Dillikannan, Sumayh Aljameel, Nida Irfan Ullah Khan, Malak Aslam, Eman Aljabri, Alsulmi, Afnan Almoammar, Lubna Alhenaki, Heba Kurdi, Chansik An, Hyunsun Lim, Dong-Wook Kim, Jung, Hyun Chang, Yoon, Jung Choi, Seong Kim, Arun Prakash, V, J Xavier, G Ramesh, T Maridurai, K Kumar, R Sam, Raj, P Aurtherson, Babu, Teja Bhanu, Karthikeyan Nalla, Kulmani Srinivasan, Yuvarajan Mehar, Devarajan, Karan Bhansali, R Kamlesh, Balinge, U Subodh, Raut, A Shubham, M Deshmukh, Senthil, C Kumar, Pundlik Kumar, Bhagat, Abdelkader Dairi, Fouzi Harrou, Abdelhafid Zeroual, Mohamad Hittawe, Ying Sun, B Deepanraj, N Senthilkumar, D Mala, A Sathiamourthy, Bellappu Jayanth, Melvin Venkat, Gopal Victor Depoures, Damodharan Kaliyaperumal, Dilipsingh Dillikannan, Kumaran Jawahar, Ganesha Palani, Meravanigee Prasad, Shivappa, Maya John, Hadil Shaiba, Manjunath Kamath, Krishna Subha, Rao, Jaison, Sridhar, Kasthuri, Gopinath, Shantanu Sivaperumal, Patil, Sarthak Maggu, Vijander Singh, Mahdi Mahdavi, Hadi Choubdar, Erfan Zabeh, Michael Rieder, Safieddin Safavi-Naeini, Zsolt Jobbagy, Amirata Ghorbani, Catharine Paules, D Hilary, Anthony Marston, Fauci, S Rajasekaran, D Damodharan, K Gopal, B Kumar, Melvin Victor, De Poures, A Rajesh
Abstract
Aim: The main objective of this study is to improve the accuracy of COVID-19 prediction and evaluation. Materials and Methods: This work depends on the data segregated from Kaggle's website where the samples are divided into two groups. Each group contains 20 samples (N=20) for both the Logistic regression and Support vector machine algorithms in accordance with the total sample size calculated using clinicalc.com by keeping alpha error-threshold at 0.05, confidence interval at 95%, enrolment ratio as 0:1, and G power at 80%. This involves training the data with validating 20 validations ranging from 5 to 24 in MatLab 2021a. Results: The accuracy, sensitivity, and precision rates are compared using the SPSS Software and Independent sample T-test. The Logistic regression has better accuracy, sensitivity, and precision of 95.98%,94,65%, 96.20% (P<0.001) respectively compared to the Support vector machine where 91.25% of accuracy (P<0.001), 93.93% of sensitivity (P<0.001), and 86.11% of precision (P<0.001). Conclusion: The Logistic regression algorithm produces superior outcomes than the Support vector machine algorithm.