Analysis and Comparison for Innovative Prediction Technique of COVID-19 using Logistic Regression algorithm over Support Vector Machine Algorithm with Improved Accuracy.

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.

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