THMI-FS-Stack: A Hybrid Imputation and Feature Selection with Stacking Ensemble for Avian Influenza Outbreak Prediction

Timely prediction of zoonotic disease outbreaks, particularly Highly Pathogenic Avian Influenza (HPAI), is critical for real-time epidemiological surveillance and pandemic pre-paredness. However, real-world avian surveillance datasets often suffer from missing values, high dimensionality, and inconsistent feature distributions, leading to unreliable predictions. This study proposes THMI-FS-Stack, a modular and interpretable machine learning pipeline that integrates hybrid data imputation, scalable feature selection, and ensemble classification for outbreak forecasting. The first stage, THMI-CB, employs a two-layer imputation framework combining statistical techniques (Mode, Hot Deck, KNN) and machine learning models (Bayesian Networks and CatBoost), achieving an F1-score of 0.91. The second stage, Hybrid-FS-ML, combines filter-based ranking (Mutual Information, Chi-Square, mRMR) with wrapper-based optimization using a Genetic Algorithm, achieving a 72% dimensionality reduction and an F1-score of 0.96. The final component is a stacking ensemble classifier that uses Random Forest and XGBoost as base learners and Logistic Regression as the meta-learner, yielding an F1-score of 0.92, accuracy of 0.93, and AUC-PR of 0.89. Evaluated on the Wild Bird HPAI dataset with 5-fold stratified cross-validation, THMI-FS-Stack consistently outperforms baseline models. Its robust architecture, low computational cost (runtime of 28–36s), and strong generalization ability make it highly suitable for noisy, incomplete epidemiological data in wildlife surveillance dashboards and early-warning systems.

@article{Murthy2025,
  title     = {THMI-FS-Stack: A Hybrid Imputation and Feature Selection with Stacking Ensemble for Avian Influenza Outbreak Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {V. S. V. S. Murthy and J. N. V. R. Swarup Kumar and Srinivas Gorla},
  doi       = {10.14569/IJACSA.2025.0161195},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161195}
}