Forecasting Dengue Fever Using Machine Learning Regression Techniques

With the increase in life-threatening viral diseases, the need for extensive research on its causes, recovery, and methods of prevention becomes crucial. Some of these diseases are dangerous and sometimes they might cause death. Dengue Fever remains one of the important public health issues expanded several areas all around the world. Dengue Fever spread could be affected by several factors such as climate conditions. In this paper, we analyze a weather-related dataset to predict the number of illness cases per week in the cities of San Juan and Iquitos by using several machine learning regression algorithms. To achieve this, we utilized and compared different machine learning regression techniques, the performance is evaluated using the Mean Absolute Error (MAE). As a result, the Poisson Regression Model achieved the best ratios and the lowest mean absolute error ratio of 25.6%.