Machine-Learning Prognostic Models From The 2018–2020 Ebola Outbreak in Democratic Republic of Congo

The Ebola virus disease epidemic in the Democratic Republic of the Congo (DRC) from 2018 to 2020 resulted in 3481 cases (both suspected and confirmed) and 2299 deaths. The WHO declared the sickness a global health emergency. The majority of the patients were known to have died before the antibodies could respond. This highlights the need to improve the disease’s diagnosis and prediction tools. The goal of this paper is to assess and enhance the accuracy of Ebola prediction algorithms using a variety of inputs. The input is based on the patient’s symptoms in the early stages of the condition. Data mining techniques used in this study include Decision Trees, KNN, Support Vector Machine, Random Forest, and Gradient Boosting classifier. The experimental findings illustrate the accuracy of each classification technique, with Support Vector Classification providing the best predictive model for both diagnosis and prognosis with 0.88 accuracy. We will include these models into an Ebola prediction web app with an API in Flask (Python), which will aid medical practitioners and people in the early diagnosis of illness.