An Optimized Ensemble Model for COVID Detection

The entire world has gone through a pandemic situation due to the spread of novel corona virus. In this paper, the authors have proposed an ensemble learning model for the classification of the subjects to be infected by coronavirus. For this purpose, five types of symptoms are considered. The dataset contains 2889 samples with six attributes and is collected from the Kaggle database. Three different types of classifiers such as Support vector machine (SVM), Gradient boosting, and extreme gradient boosting (XGBoost) are considered for classification purposes. For improving the learning strategy and performance of the proposed models subjected to accuracy, the learning rates are varied for each node of the tree-based ensemble classifiers. Also, the hyperparameters of the XGBoost model are optimized by applying the Bayesian optimization (BO) technique. The best accuracy in SVM classifier is found as 91.69%. 96.58% accuracy is obtained in the modified gradient boosting model. The optimized XGBoost model is providing 100% accuracy which is better than other.