Development of optimized ensemble classifier for dengue fever prediction and recommendation system

Abstract

Dengue fever needs to be managed, which is considered as an important issue in health, nowadays. An efficient allocation of resources is mostly challenging owing to the external and internal components that have imposed non-linear fluctuations in the occurrence of dengue fever. Various machine learning and deep learning algorithms are developed for supporting the healthcare sector analysis, which has assured the efficiency and significance of the exact prediction of diseases and also ensured the minimal mortality rate. The core concept of this work is to implement an early-warning system for forecasting dengue fever and providing the proper recommendation system through intelligent techniques. Here, the enhanced prediction of dengue fever and recommendation is the main objective of this paper. In the data pre-processing stage, outlier removal and missing data filling are the main techniques to enhance the quality of data. Further, the optimal feature selection is performed using Neighbour Count-based Dragonfly Electric Fish Optimization (NC-DEFO). These acquired features are subjected to the Optimized Ensemble Classifier (OEC), in which “Convolutional Neural Network, Artificial Neural Network, and Support Vector Machine” are used according to the high ranking. Once the dengue fever in a particular area is predicted, the proper medical recommendation is provided in that area in terms of precaution steps, immunity-boosting remedies, drug prescription, spreading avoidance, etc. The evaluated results of the developed model with different comparative algorithms have shown efficient and superior outcomes from the implemented model, which seems to be accurate for the earlier diagnosis of dengue fever.