Analyzing the Death Ratio of Covid Patients using Multiple Logistic Regression in Comparison with Linear Regression for Improving Accuracy.

Aim: The aim of the study is to analyze the death ratio of covid patients using Novel Multiple Logistic Regression and linear regression which comes under supervised learning. Materials and Method: Accuracy is analyzed for a covid dataset of size 239 places. Analyzingthe death ratio of covid patients is performed by a Novel Multiple Logistic Regression of sample size (N=35) and Linear Regression of sample size (N=35), obtained using the G-power value of 80%. These are supervised learning algorithms. Result: Novel Multiple Logistic Regression accuracy is 96% which is comparatively higher than LR with an accuracy of 86%. The significance value is determined as p=0.030 (p<0.05) for accuracy. Conclusion: Novel Multiple Logistic Regression performs better in finding accuracy when compared to Linear Regression. [ABSTRACT FROM AUTHOR]