Background: Ensemble building is a popular method for improving model accuracy for
classification problems as well as regression.
Objective: In this research work, we propose a sequential ensemble model to predict the number of
incidences for communicable diseases like influenza, hand foot and mouth disease (HFMD), and
diarrhea and compare it with applied models for prediction.
Methods: The weekly dataset of the three diseases, namely, influenza, HFMD, and diarrhea, are
collected from the official government site of Hong Kong from the year 2010 to 2018. The data
was preprocessed by taking log transformation and z-score transformation. The proposed
sequential ensemble model is applied to the processed dataset to predict future occurrences.
Results: The result of the proposed ensemble model is compared against standard support vector
regression (SVR) using different error metrics such as root mean square error (RMSE), mean
absolute error (MAE) and mean absolute percentage error (MAPE). In the case of all the threedisease
datasets, the proposed ensemble model gives better results in comparison to the standard
Conclusion: The main objective of this research work is to minimize the prediction error; the
proposed sequential ensemble model has shown a significant result in terms of prediction errors.