%D 2025
%V 31
%L unic16824
%N 1
%J Health Informatics Journal
%T Ventilator pressure prediction employing voting regressor with time series data of patient breaths
%R doi:10.1177/14604582241295912
%A Ali Raza
%A Furqan Rustam
%A Hafeez Ur Rehman Siddiqui
%A Emmanuel Soriano Flores
%A Juan Luis Vidal Mazón
%A Isabel de la Torre Díez
%A María Asunción Vicente Ripoll
%A Imran Ashraf
%K COVID-19, deep learning, machine learning, mechanical ventilation, ventilator pressure prediction
%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.