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Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 3

The RNN-Based Deep Learning Model Design to Predict ICU Occupancy Rate and Number of Intubated Patients for Effective Healthcare System Management

Sertaç Savaş(ERCIYES UNIVERSITY, FACULTY OF ENGINEERING)0000-0001-8096-1140
Mehmet Safa Bingöl(NIGDE OMER HALISDEMIR UNIVERSITY, FACULTY OF ENGINEERING)0000-0002-4188-2025
Ahmet Kırnap(ERCİYES ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-7685-5297
Şahin Yıldırım(ERCIYES UNIVERSITY, FACULTY OF ENGINEERING)0000-0002-7149-3274
Pages
1008–1021
Publication Date
2026-05-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the GRU model achieves the best performance with RMSE values of 15.7277 and 6.6494 for intensive care and intubated patient numbers, respectively, using an 80/20% train/test ratio.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Epidemics have been one of the most significant health threats in human history.
📊
Key Findings & Data:Today, as new epidemics such as COVID-19 and Monkeypox emerge, it is critical for healthcare systems to be prepared for such crises.
💡
Conclusion & Impact:The results show that the GRU model achieves the best performance with RMSE values of 15.7277 and 6.6494 for intensive care and intubated patient numbers, respectively, using an 80/20% train/test ratio.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Epidemic progression prediction · Machine learning · LSTM

Abstract

Epidemics have been one of the most significant health threats in human history. Today, as new epidemics such as COVID-19 and Monkeypox emerge, it is critical for healthcare systems to be prepared for such crises. Predicting the progression of an epidemic is essential for healthcare systems to respond effectively. In this study, an artificial intelligence model design is proposed to predict the number of intubated and intensive care unit patients during a pandemic. LSTM, BiLSTM and GRU models belonging to the RNN family of machine learning algorithms are used in the predictor design and the grid search method is applied for hyperparameter optimization. In the design of the proposed model, the number of patients intubated and treated in intensive care during the COVID-19 pandemic in Türkiye is used as the dataset. The results show that the GRU model achieves the best performance with RMSE values of 15.7277 and 6.6494 for intensive care and intubated patient numbers, respectively, using an 80/20% train/test ratio. Similarly, GRU provides the highest accuracy with RMSE values of 9.9085 and 7.0271 for the same datasets using a 90/10% train/test ratio. These findings reveal that the simple structure of the GRU model, with fewer parameters and reduced computational complexity, is compatible with the dataset and provides better generalization capability, demonstrating that the deep learning model we designed can be used to predict the number of intensive care and intubated patients in order to facilitate healthcare system management in epidemic processes.

Özet

Epidemics have been one of the most significant health threats in human history. Today, as new epidemics such as COVID-19 and Monkeypox emerge, it is critical for healthcare systems to be prepared for such crises. Predicting the progression of an epidemic is essential for healthcare systems to respond effectively. In this study, an artificial intelligence model design is proposed to predict the number of intubated and intensive care unit patients during a pandemic. LSTM, BiLSTM and GRU models belonging to the RNN family of machine learning algorithms are used in the predictor design and the grid search method is applied for hyperparameter optimization. In the design of the proposed model, the number of patients intubated and treated in intensive care during the COVID-19 pandemic in Türkiye is used as the dataset. The results show that the GRU model achieves the best performance with RMSE values of 15.7277 and 6.6494 for intensive care and intubated patient numbers, respectively, using an 80/20% train/test ratio. Similarly, GRU provides the highest accuracy with RMSE values of 9.9085 and 7.0271 for the same datasets using a 90/10% train/test ratio. These findings reveal that the simple structure of the GRU model, with fewer parameters and reduced computational complexity, is compatible with the dataset and provides better generalization capability, demonstrating that the deep learning model we designed can be used to predict the number of intensive care and intubated patients in order to facilitate healthcare system management in epidemic processes.

🔖

Cite This Article

Savaş, S., Bingöl, M. S., Kırnap, A., Yıldırım, Ş. (2026). The RNN-Based Deep Learning Model Design to Predict ICU Occupancy Rate and Number of Intubated Patients for Effective Healthcare System Management. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 3, 1008–1021. https://doi.org/10.34248/bsengineering.1844105

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