Mahajan, Asmita and Sharma, Nonita and Aparicio Obregón, Silvia and Alyami, Hashem and Alharbi, Abdullah and Anand, Divya and Sharma, Manish and Goyal, Nitin UNSPECIFIED, UNSPECIFIED, silvia.aparicio@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, divya.anand@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2022) A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction. Mathematics, 10 (10). p. 1714. ISSN 2227-7390
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Abstract
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
| Item Type: | Article |
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| Uncontrolled Keywords: | autoregressive integrated moving average; epidemiology; exponential smoothing; ensemble; gradient boosting; infectious disease; neural network autoregression; pandemic; stacking |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 27 May 2022 09:49 |
| Last Modified: | 11 Jul 2023 06:37 |
| URI: | http://repositorio.funiber.org/id/eprint/2118 |
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