An Ensemble Model (Simple Average) for Malaria Cases in North India

Abstract

Malaria is an infectious disease borne due to mosquitoes that attacks humans and other animals’ bodies. Malaria is a part of the plasmodium group caused by single-celled microorganisms. This study proposes the use of ensemble model using the three regression algorithms that are linear regression, support vector machine (SVM), and auto-Arima techniques and comparing their results. Predictions of plasmodium virus cases are made with the use of linear regression, support vector machine, and auto-Arima algorithms. The accuracy of prediction is measured by calculating the explained variance score, mean squared error rate, and root mean squared error rate. Our aim is to get better prediction results compared to the individual algorithms by combining the results of these individual models. The proposed work determines the accuracy of linear regression, support vector machine, and auto-Arima and ensembles together to find the trend of prediction using simple Average. A comparison of performance among the three regression techniques indicated the SVM model performs the best and has small RMSE and MAE values. But, by introducing the technique of ensemble modeling using simple average, combining the prediction of these three algorithms results in the lowest RMSE and MAE values.