This book investigates the application of machine learning algorithms for the early prediction of epidemic diseases, specifically focusing on dengue. The authors address the challenge posed by dengue, a mosquito-borne viral infection exacerbated by atmospheric conditions, and propose machine learning models to forecast outbreaks. By utilizing weather parameters such as temperature and humidity, the study aims to provide a predictive framework to mitigate transmission within communities. The research employs algorithms like Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Decision Tree (DT), with pre-processing of weather data via label encoding before model training and performance evaluation.

This work presents a focused application of machine learning techniques to a critical public health issue, dengue prediction. The methodology described, involving various established ML algorithms and weather data, suggests a data-driven approach to forecasting disease outbreaks. The study's emphasis on early prediction indicates its potential utility for public health interventions and resource allocation.