Analysis and Comparison for Innovative Prediction Technique of COVID-19 using Decision Tree Algorithm over the Support Vector Machine Algorithm with Improved Accuracy

Abstract

Aim: The primary goal of this research is to increase the accuracy of COVID-19 prediction and its analysis. Materials and Method: This study relied on data collected from Kaggle’s website and samples are divided into two groups, GROUP 1 (N=20) for the Decision tree and GROUP 2 (N=20) for the Support Vector Machine (SVM) in accordance with the total sample size calculated using clinical.com by keeping alpha error-threshold value 0.05, 95% confidence interval, enrolment ratio as 0:1, and G power at 80%. It involves the software implementation program in MatLab 2021a validating with 20 validations. Results: The accuracy, sensitivity, and precision rates are compared using Statistical Package for the Social Sciences (SPSS) software and an Independent sample T-Test. In comparison to the two, the Decision tree 93.91% accuracy, 94.33% sensitivity, 92% precision with P=0.001 ((p<0.05) produces a superior outcome to the Support Vector Machine 91.25% accuracy, 93.93% sensitivity, 86.11% precision (P<0.001)). Conclusion: The decision tree algorithm produces better results compared to the Support Vector Machine.