Several Classification and Recommendations Methods Used in Dengue Fever Prediction System

Mosquitos influence dengue fever, and the dengue virus is a universal community health issue worldwide. An analysis and prediction are required to resolve the effects of the dengue virus in communities. The main motive of this article is to recognize the classification or recommendation methods based on machine learning (ML) and deep learning (DL) for predicting and detecting dengue fever. The classification methods such as SVM, KNN, DT, and naïve bayes are used to perform experimental results. In this article, a comparison of these methods is executed, and SVM achieves a better accuracy rate. This method is highest accurate and suitable for predicting the dengue virus. The naïve bays is an effective method for better performance with less time-consuming. This method takes 0.01 seconds and reduces the probability of errors. The techniques like DT, KNN, and naïve Bayes provide 55.5%, 96%, and 72% accuracy, respectively. The SVM, DT, and naïve bayes consumed the time of 0.16sec, 0.05sec, and 0.01sec, respectively.