Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms

Abstract

COVID-19, Corona Virus Diasease-2019, belongs to genus of Coronaviridae. A virus with no vaccine creating unpredictable havocs in the human lives and financial and economic systems in every country throughout the world. It is precariously halted everything in the society mercilessly. An analysis on COVID-19 datasets to understand which age group is mostly effected due to COVID-19. Different prediction models are built using machine learning algorithms and their performances are computed and evaluated. Random Forest Regressor and Random Forest Classifier outperformed the other machine learning models like SVM, KNN+NCA, Decision Tree Classifier, Gaussian Naïve Bayesian Classifier, Multilinear Regression, Logistic Regression and XGBoost Classifier.

Applications of artificial intelligence in battling against covid-19: A literature review

“…In [122] a machine learning algorithm is presented to assist clinical decision making during the pandemic. In [123] , different machine learning models including SVM, KNN, Decision Tree, Gaussian Naive Bayesian, etc. are used to predict which age groups are mode affected by the disease.…”

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

“…The Accuracy achieved was 97.36% with 97.65% sensitivity. Prakash et al [121] introduced an ML-based algorithm which is the preferred approach for COVID-19 case assessment and laboratory testing of molecules of respiratory tract samples.To evaluate their features and create ML models for performance assessment, the two datasets Covid-19-India and COVID-19-data from Kaggle were used. The accuracy achieved was around 96.7%.…”

Forecasting COVID-19 cases in the Philippines using various mathematical models

“…Aside from model fitting, RF has been extensively used in various machine learning forecasting studies such as electricity load, employee turnovers, and even on epidemics forecasting [18, 31, 20, 36]. Recently, it has also been used to analyze, predict, and evaluate COVID-19 in India, and COVID-19 patient health [27, 26]. Based from [27] on COVID-19 predictions in India, results from the random forest machine learning algorithm outperformed other machine learning methods.…”

“…Recently, it has also been used to analyze, predict, and evaluate COVID-19 in India, and COVID-19 patient health [27, 26]. Based from [27] on COVID-19 predictions in India, results from the random forest machine learning algorithm outperformed other machine learning methods. Thus, we implement RF to forecast the COVID-19 cumulative cases of infection in the Philippines.…”