Traditional Machine Learning based on Atmospheric Conditions for Prediction of Dengue Presence

The dengue virus has become an increasingly
critical problem for humanity due to its extensive spread.
This is transmitted through a vector that sprouts in
certain climatic conditions (tropical and subtropical
climates). The transmission of the disease can be
associated with certain climatic variables that reinforce
the outbreak. Data were collected on dengue cases by
epidemiological week registered in Loreto-Peru from
January 1, 2016, to January 31, 2022. Likewise, data on meteorological variables (maximum and minimum temperature; dry and humid bulb temperature; wind speed and total precipitation in the area). In this study, four Machine learning modeling techniques were considered: Support Vector Machine (SVM), Decision Tree, Random Forest and AdaBoost; and the parameters defined to evaluate the models are: Accuracy, Precision, Recall and F-1. As a result, optimal AUC values were obtained in a range from 0.818 to 0.996 for the SVM, Random Forest and AdaBoost algorithms, likewise, in all weather stations the ROC curve showed good performance for all models, except for the Decision Tree algorithm. As a conclusion for this study, we propose the optimal model to associate dengue cases with climatic conditions is SVM.