Prediction of Monkeypox Disease Using Biogeography Based Optimization Methodology

Monkeypox, a rare viral illness predominantly found in Central and West Africa, presents significant public health concerns. Detecting and predicting monkeypox outbreaks early is essential for effective disease management. In this study, we propose a new method for predicting monkeypox using Biogeography-Based Optimization (BBO) alongside machine learning techniques. We used clinical features and demographic data dataset to train and evaluate our predictive model. BBO was employed to select the most relevant subset of features from the dataset, improving the model’s performance. Our findings demonstrate the efficacy of our approach, achieving high accuracy in predicting monkeypox outbreaks. Moreover, we emphasize the importance of feature selection in enhancing the interpretability and applicability of predictive models for infectious diseases. This research contributes to advancing predictive analytics in epidemiology and offers valuable insights for disease surveillance and public health interventions.