A Weighted Ensemble Model for Prediction of Infectious Diseases

The ensemble building is a common method to improve the performance of the model in case of regression as well as classification.

In this paper we propose a weighted average ensemble model to predict the number of incidence for infectious diseases like typhoid and compare it with applied models for prediction.

The Monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. The data was processed by three regressions such as support vector regression, neural network and linear regression.

To evaluate the prediction error and compare it with different models, different performance measures have been used such as MSE, RMSE and MAE and it was found that proposed ensemble method performed better in terms of forecast measures.

Our main aim in this paper is to minimize the prediction error; the resulting proposed weighted average ensemble model has shown a significant result in terms of prediction errors.