Dengue Fever Outbreak Prediction Using Machine Learning Models: A Comparative Study

In many parts of the world, dengue fever is a serious health risk that is spread by mosquitoes. Officials in charge of public health can take proactive steps to stop the spread of dengue by accurately forecasting outbreaks. A comparison of various machine learning models for forecasting dengue outbreaks is presented in this research report. The performance of machine learning algorithms such as Random Forest Regression, Support Vector Regression, and Long Short-Term Memory Neural Networks is compared using four datasets from various parts of the world. Metrics like Mean Squared Error, Mean Absolute Error, and R-Squared are used to assess each model's performance. The study also analyzes the most crucial environmental and epidemiological elements and looks into the significance of various characteristics in predicting dengue outbreaks. Overall, this study contributes to the growing body of work on utilizing machine learning models to predict dengue outbreaks.