TY  - JOUR
AV  - public
A1  - Mahajan, Asmita
A1  - Sharma, Nonita
A1  - Aparicio Obregón, Silvia
A1  - Alyami, Hashem
A1  - Alharbi, Abdullah
A1  - Anand, Divya
A1  - Sharma, Manish
A1  - Goyal, Nitin
KW  - autoregressive integrated moving average; epidemiology; exponential smoothing; ensemble; gradient boosting; infectious disease; neural network autoregression; pandemic; stacking
TI  - A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction
ID  - uneatlantico2118
VL  - 10
SN  - 2227-7390
UR  - http://doi.org/10.3390/math10101714
Y1  - 2022/05//
JF  - Mathematics
N2  - Infectious Disease Prediction aims to anticipate the aspects of both seasonal epidemics and future pandemics. However, a single model will most likely not capture all the dataset?s patterns and qualities. Ensemble learning combines multiple models to obtain a single prediction that uses the qualities of each model. This study aims to develop a stacked ensemble model to accurately predict the future occurrences of infectious diseases viewed at some point in time as epidemics, namely, dengue, influenza, and tuberculosis. The main objective is to enhance the prediction performance of the proposed model by reducing prediction errors. Autoregressive integrated moving average, exponential smoothing, and neural network autoregression are applied to the disease dataset individually. The gradient boosting model combines the regress values of the above three statistical models to obtain an ensemble model. The results conclude that the forecasting precision of the proposed stacked ensemble model is better than that of the standard gradient boosting model. The ensemble model reduces the prediction errors, root-mean-square error, for the dengue, influenza, and tuberculosis dataset by approximately 30%, 24%, and 25%, respectively
IS  - 10
ER  -