Comparison of Machine Learning Models for predicting Covid-19 patients' recovery in India

The pandemic Covid-19 is a name coined by WHO on 31st December 2019. This devastating illness was carried on by a new coronavirus known as SARS-COV-2. Most of the research has focused on estimating the total number of cases and mortality rate of COVID-19. Due to this, people across the world were stressed out by observing the growing number of cases every day. As a means of maintaining equilibrium, this paper aims to identify the best way to predict the number of recovered cases of Coronavirus in India. Dataset was divided into two parts: training and testing. The training dataset utilised 70% of the dataset, and the testing dataset utilised 30%. In this paper, we applied 10 machine learning techniques i.e. Random Forest Classifier (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Gradient Boosting Classifier (GBM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K Neighbour Classifier (KNN), Decision Tree Classifier (DT), SVM - Linear and Ada-Boost Classifier in order to predict recovered patients in India. Our study suggests that Random Forest Classifier outperforms other machine learning models for predicting the recovered Coronavirus patients having an accuracy of 0.9632, AUC of 0.9836, Recall of 0.9640, Precision of 0.9680, F1 Score of 0.9617 and Kappa of 0.9558. © 2022 IEEE.

Prediction of Happiness Score of Countries by Considering Maximum Infection Rate of People by COVID-19 using Random Forest Algorithm

In this paper, the relationship between COVID-19 Maximum Infection Rate (MIR) and the happiness indicators has been investigated for the prediction of Happiness Score of Countries using Random Forest (RF) algorithm. The per-formance of the proposed algorithm is also compared against five other algorithms such as Linear Regression (LR), Ada Boost Classifier (ABC), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (NB) and Logistic Regression. The comparison of performance includes parameters like training accuracy, testing accuracy and computation time. It is clear from the observation that the proposed approach is superior to others. Then the parameters like MAE, MSE, RMSE, R2 Score, Adjusted R2 Score is calculated. This proposed algorithm can be used for other classification and regression work involving large amount of data with missing values like COVID- 19 datasets. © 2022 IEEE.