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A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction

Author

Listed:
  • Asmita Mahajan

    (Indian Institute of Technology Roorkee, Roorkee 247667, India)

  • Nonita Sharma

    (Department of Information Technology, IGDTUW Delhi, New Delhi 110006, India)

  • Silvia Aparicio-Obregon

    (Faculty of Social Sciences and Humanities, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
    Faculty of Engineering, Universidade Internacional do Cuanza, Estrada Nacional, 250, Bairro Kaluapanda, Cuito-Bié 250, Angola)

  • Hashem Alyami

    (Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Abdullah Alharbi

    (Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Divya Anand

    (School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India
    Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain)

  • Manish Sharma

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India)

  • Nitin Goyal

    (Computer Science Engineering Department, Shri Vishwakarma Skill University, Palwal 121102, India)

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.

Suggested Citation

  • Asmita Mahajan & Nonita Sharma & Silvia Aparicio-Obregon & Hashem Alyami & Abdullah Alharbi & Divya Anand & Manish Sharma & Nitin Goyal, 2022. "A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1714-:d:817573
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    References listed on IDEAS

    as
    1. Evan L Ray & Nicholas G Reich, 2018. "Prediction of infectious disease epidemics via weighted density ensembles," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-23, February.
    2. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    3. Xingyu Zhang & Tao Zhang & Alistair A Young & Xiaosong Li, 2014. "Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
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    Cited by:

    1. Yunlong Ding & Di-Rong Chen, 2023. "Optimization Based Layer-Wise Pruning Threshold Method for Accelerating Convolutional Neural Networks," Mathematics, MDPI, vol. 11(15), pages 1-13, July.

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