Abstract
This paper presents the results on the model creation for predicting dengue cases in Baguio City, Philippines. In this paper, machine learning models such as Random Forest, Linear Regression, Support Vector Machines (SVM), and Gradient Boosting are tested for their prediction accuracy in modeling the number of dengue cases in five barangays of Baguio City that have the most number of dengue cases yearly. In modeling the monthly dengue cases, meteorological factors and time related factors, were considered as input features to the models. Results have shown that the least performing model is the SVM. The linear regression achieve optimal performance with only minimum feature inputs while the Random Forest and the Gradient Boosting methods achieve comparable prediction errors to that of the linear regression models. Furthermore, optimal results of the random forest was achieved with Time-related features and the meteorological factors, specifically the rainfall (RF) and relative humidity (RH). These two models also achieved lower errors in predicting dengue cases specifically during the peak months, as compared to using linear regression models.
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Addawe, J.C., Caro, J.D.L., Juayong, R.A.B. (2023). Machine Learning Methods for Modeling Dengue Incidence in Local Communities. In: Krouska, A., Troussas, C., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 2nd International Conference (NiDS 2022). NiDS 2022. Lecture Notes in Networks and Systems, vol 556. Springer, Cham. https://doi.org/10.1007/978-3-031-17601-2_38
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