@article{unic16824,
          number = {1},
            year = {2025},
         journal = {Health Informatics Journal},
          author = {Ali Raza and Furqan Rustam and Hafeez Ur Rehman Siddiqui and Emmanuel Soriano Flores and Juan Luis Vidal Maz{\'o}n and Isabel de la Torre D{\'i}ez and Mar{\'i}a Asunci{\'o}n Vicente Ripoll and Imran Ashraf},
          volume = {31},
           title = {Ventilator pressure prediction employing voting regressor with time series data of patient breaths},
           month = {Enero},
        keywords = {COVID-19, deep learning, machine learning, mechanical ventilation, ventilator pressure prediction},
             url = {http://repositorio.unic.co.ao/id/eprint/16824/},
        abstract = {Objectives: Mechanical ventilator plays a vital role in saving millions of lives. Patients with COVID-19 symptoms need a ventilator to survive during the pandemic. Studies have reported that the mortality rates rise from 50\% to 97\% in those requiring mechanical ventilation during COVID-19. The pumping of air into the patient?s lungs using a ventilator requires a particular air pressure. High or low ventilator pressure can result in a patient?s life loss as high air pressure in the ventilator causes the patient lung damage while lower pressure provides insufficient oxygen. Consequently, precise prediction of ventilator pressure is a task of great significance in this regard. The primary aim of this study is to predict the airway pressure in the ventilator respiratory circuit during the breath. Methods: A novel hybrid ventilator pressure predictor (H-VPP) approach is proposed. The ventilator exploratory data analysis reveals that the high values of lung attributes R and C during initial time step values are the prominent causes of high ventilator pressure. Results: Experiments using the proposed approach indicate H-VPP achieves a 0.78 R2, mean absolute error of 0.028, and mean squared error of 0.003. These results are better than other machine learning and deep learning models employed in this study. Conclusion: Extensive experimentation indicates the superior performance of the proposed approach for ventilator pressure prediction with high accuracy. Furthermore, performance comparison with state-of-the-art studies corroborates the superior performance of the proposed approach.}
}