An Artificial Intelligence Approach for Forecasting Ebola Disease

Abstract

Abstract The abrupt explosion of the Ebola virus in 2014 in Western Africa was one of the world’s most widespread and deadliest epidemics with the highest number of casualties being reported in the regions of West and Central Africa. Ebola, a fatal hemorrhagic fever syndrome, is caused by the Ebola virus (EBOV). The World Health Organization proclaimed the disease as a world healthcare crisis. In most of the cases, the patients are known to have died before the antibodies could respond. This indicates the need to improve upon the diagnosis and prediction techniques available for this disease. This paper aims to analyze and improve upon the accuracy of the prediction systems for the Ebola disease using several inputs. The input relies on the symptoms shown by the patient during the early stages of the disease. The data mining techniques employed to carry out this research include Decision Trees; Bagging classifier, KNN, Support Vector Machine, Stochastic Gradient Descent classifier, Logistic Regression, Random Forest, Gradient Boosting classifier, Ridge Classifier, and Hybrid Neural Networks. The hybrid models recommended in this study include the use of classifiers, namely, Stochastic Gradient Descent, Random Forest and KNN classifier. The experimental results show the accuracy obtained by each classification technique and the hybrid models that were applied to the dataset.