Analyzing the Death Ratio of Covid Patients using Multiple Logistic Regression in Comparison with Linear Regression for Improving Accuracy
DOI:
https://doi.org/10.47750/pnr.2022.13.S04.032Keywords:
Big Data Analytics, Supervised learning, Death ratio, Linear Regression, Novel Multiple Logistic Regression, Machine Learning.Abstract
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.
