%0 Journal Article
%@ 1460-4582
%A Raza, Ali
%A Rustam, Furqan
%A Siddiqui, Hafeez Ur Rehman
%A Soriano Flores, Emmanuel
%A Vidal Mazón, Juan Luis
%A de la Torre Díez, Isabel
%A Ripoll, María Asunción Vicente
%A Ashraf, Imran
%D 2025
%F unic:16824
%J Health Informatics Journal
%K COVID-19, deep learning, machine learning, mechanical ventilation, ventilator pressure prediction
%N 1
%T Ventilator pressure prediction employing voting regressor with time series data of patient breaths
%U http://repositorio.unic.co.ao/id/eprint/16824/
%V 31
%X 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.