Epidemic Modeling with a Hybrid RF-LSTM Method for Healthcare Demand Prediction

Accurate resource demand forecasts are necessary for sustainable healthcare systems to preserve flexibility and efficiency as well as to provide services in a professional manner. In this work, we propose an integrated Random Forest/Long Short-Term Memory (RF-LSTM) model for predicting Saudi Arabia's national healthcare resource demand. It combines non-linear feature extraction and temporal sequence learning. The integrated model employs governmental epidemiological and operational data from 2020 to 2024 to capture both short-term and long-term volatility and sustainability trends. The results demonstrate significant improvements in predictive accuracy compared with single-model baselines, such as Autoregressive Integrated Moving Average (ARIMA), Random Forest (RF), and Long Short Term Memory (LSTM), with reductions in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for up to 22% and 18% compared with ARIMA, and by 12% and 9% relative to the best single model, which is LSTM, respectively A statistical analysis using one-way ANOVA confirmed the robustness of the hybrid method. Furthermore, residual plots were examined to verify model assumptions and visually assess the uniformity of prediction errors, thereby validating the results. These findings suggest that integrated AI-based prediction models can effectively facilitate capacity planning, enhance resource allocation, and contribute to achieving the objectives of Saudi Vision 2030 for a resilient, data-driven healthcare system.

@article{Alshammari2025,
  title     = {Epidemic Modeling with a Hybrid RF-LSTM Method for Healthcare Demand Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Budor Alshammari and Bassam Zafar},
  doi       = {10.14569/IJACSA.2025.0161297},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161297}
}