Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

Author:

Ahmed Fatma Refaat,Alsenany Samira Ahmed,Abdelaliem Sally Mohammed Farghaly,Deif Mohanad A.

Abstract

AbstractThe utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant.

Funder

Princess Nourah Bint Abdulrahman University

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference70 articles.

1. Alam, M. J., Rabbi, J., & Ahamed, S. Forecasting pressure of ventilator using a hybrid deep learning model built with Bi-LSTM and Bi-GRU to simulate ventilation. arXiv Prepr. arXiv:2302.09691 (2023).

2. Strodthoff, C., Frerichs, I., Weiler, N., & Bergh, B. Predicting and simulating effects of PEEP changes with machine learning. medRxiv, pp. 2001–2021 (2021).

3. Schalekamp, S. et al. Model-based prediction of critical illness in hospitalized patients with COVID-19. Radiology 298(1), E46–E54 (2021).

4. Belgaid, A. Deep sequence modeling for pressure controlled mechanical ventilation. medRxiv, pp. 2003–2022 (2022).

5. Zhang, K., Karanth, S., Patel, B., Murphy, R., & Jiang, X. Real-time prediction for mechanical ventilation in COVID-19 patients using a multi-task gaussian process multi-objective self-attention network. arXiv Prepr. arXiv:2102.01147 (2021).

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