Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa

Abstract

East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe.

Investigation and Modeling of the Behavior of Temperature Characteristics of 0.3–1.1 GHz Complementary Metal Oxide Semiconductor Class-A Broadband Power Amplifiers

“…It developed rapidly and derived a series of improved and extended algorithms in recent decades. Except for modeling PA behavior, this method is widely used in mortality prediction due to COVID-19 [19], coronary artery disease detection [20], wireless communication channel scenarios identification [21], image classification [22], power quality disturbance classification [23], etc. The regression is revealed as a branch of SVM.…”

A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm

“…The artificial intelligence prediction models ANFIS, feed-forward neural network (FFNN), support vector machine (SVM), and multi-linear regression (MLR) have been presented by Abegaz and Etikan [27] to evaluate the mortality of COVID-19 in East Africa. In their analysis, the ensemble ANFIS outperformed linear techniques in terms of accuracy.…”