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

•To develop new ensemble learning strategy-based dengue fever prediction and recommendation system using the heuristic-based algorithm for predicting the dengue fever and recommending the appropriate measures for patients.•To perform optimal feature selection with the proposed Neighbor Count-based Dragonfly Electric Fish Optimization (NC-DEFO) for selecting the essential features to enhance the classification efficiency by minimizing the correlation and variance between the features.•To integrate the ensemble classifier named OEC with the help of Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM) for predicting the dengue spreading among the people by considering the high ranking between the three outputs of the classifiers to make effective prediction. Here, parameter optimization takes place using the developed NC-DEFO for enhancing the effectiveness of the model.•To introduce the hybrid heuristic algorithm named NC-DEFO for selecting the optimal features, tuning the hidden neurons of CNN and ANN and number of epochs of SVM to make prediction with less errors and to provide appropriate recommendations for the patients.•To validate the proposed model with the comparison with different methods along with the convergence analysis. 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 Ne