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Predictive Analysis for Identifying Survival of Covid-19 Patients Using Support Vector Machine over Logistic Regression
K. Ganesh, Nelson Kennedy Babu C
237 - 243
10.3233/APC220032
Research Article
Advances in Parallel Computing
Volume 41: Advances in Parallel Computing Algorithms, Tools and Paradigms
Abstract

The use of Logistic Regression (LR) and Support Vector Machines (SVM) to identify the survival of Covid 19 patients in a novel way is the prime work. To forecast the survival of Covid-19 patients, The LR algorithm and the SVM algorithm are iterated 20 times with a sample size of 10.In comparison to the 86.4% accuracy of the logistic regression technique, the Support Vector Machine approach offers a 91.2% accuracy. In independent sample T-test, LR has a high level of significance. The range of this study, reveals SVM algorithm aids in data analysis and classifier performance.

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