Comparing Multiple Logistic Regression to Logistic Regression in Order to Improve Accuracy while Analyzing the Death Ratio of Covid Patients

Abstract

Aim: Objective of is based on analysis of death ratio for covid patients with Novel Multiple Logistic Regression(MLR)and logistic regression which falls under supervised learning. Materials and Method: Accuracy is analyzed for covid dataset of size 239 places.  Analyzingthe death ratio of covid patients is performed by a Novel Multiple Logistic Regression takes sample size (N=35) as well as logistic regression with available size (N=35), obtained using the G-power value 80%. These are supervised learning algorithms. Result: Novel Multiple Logistic Regression accuracy is 96% which is comparatively higher than LR with accuracy of 76%. The significance value is determined as p=0.046 (p<0. 05) for accuracy. Conclusion: Novel Multiple Logistic Regression performs well in calculating accuracy when compared to Logistic Regression. The various applications of Linear Regression are in healthcare and social sciences which are used to predict mortality in injured patients. The Various applications of Multiple Logistic Regression are the algorithm mostly used in medical and science fields to help patients in various ways.