Aim: The major goal of this research is to increase the accuracy of innovation prediction and examine the COVID-19. 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 Logistic regression and GROUP 2 (N=20) for Decision tree in accordance with the total sample size calculated using clinical.com by keeping 0.05 alpha error-threshold, 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 SPSS software and an Independent sample T-Test. In comparison the Logistic regression 95.98% accuracy with P=0.001, (p<0.05), 94.65% sensitivity (with P=0.001, (p<0.05) and 96.20% precision with P=0.001, (p<0.05) produces a superior outcome than the Decision tree 93.91% accuracy with P=0.001, (p<0.05), 94.33% sensitivity with P=0.001, (p<0.05), 92.00% precision with P=0.001, (p<0.05). Conclusion: The Logistic regression algorithm produces better results compared to the Decision tree.

Aim: The primary purpose of this study is to improve the accuracy of COVID-19 prediction and evaluation. Materials and Methods: This project is based on data extracted from Kaggle’s website, which is separated into two categories. According to the total sample size estimated by clinical.com, each group comprises 20 samples (N=20) for both the Support Vector Machine (SVM) and Neural Network methods, by keeping 0.05 alpha error-threshold, 95% confidence interval, enrolment ratio at 0:1, and G power at 80%. In MatLab 2021a, this entails training the data and verifying 20 validations ranging from 5 to 24. Results: The SPSS Software and Independent sample T-test are used to contrast the accuracy, sensitivity, and precision rates. The Neural Network has 94.55 percent accuracy (P<0.001), 93.11 percent sensitivity (P<0.001), and 95.31 percent precision (P<0.001), compared to 91.25 percent accuracy (P<0.001), 93.93 percent sensitivity (P<0.001), and 86.11 percent precision (P<0.001) for the SVM. Conclusion: The Neural Network algorithm outperforms the SVM approach in terms of results.

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.