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A Novel Machine Learning Prediction Model for Aerosol Transport in Upper 17-Generations of the Human Respiratory Tract

Author

Listed:
  • Mohammad S. Islam

    (School of Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia)

  • Shahid Husain

    (Department of Mechanical Engineering, Zakir Husain College of Engineering & Technology, Aligarh Muslim University, Aligarh 202002, India)

  • Jawed Mustafa

    (Mechanical Engineering Department, College of Engineering, Najran University, Najran P.O Box 1988, Saudi Arabia)

  • Yuantong Gu

    (Faculty of Engineering, School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia)

Abstract

The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, developing an innovative tool that accurately predicts transport behaviour and reduces computational time is essential. This study aims to develop a novel and innovative machine learning (ML) model to predict particle deposition to the lower airways. The first-ever study uses ML techniques to explore the pulmonary aerosol TD in a digital 17-generation airway model. The ML model uses the computational data for a 17-generation airway model and four standard ML regression models are used to save the computational cost. Random forest (RF), k -nearest neighbour ( k -NN), multi-layer perceptron (MLP) and Gaussian process regression (GPR) techniques are used to develop the ML models. The MLP regression model displays more accurate estimates than other ML models. Finally, a prediction model is developed, and the results are significantly closer to the measured values. The prediction model predicts the deposition efficiency (DE) for different particle sizes and flow rates. A comprehensive lobe-specific DE is also predicted for various flow rates. This first-ever aerosol transport prediction model can accurately predict the DE in different regions of the airways in a couple of minutes. This innovative approach and accurate prediction will improve the literature and knowledge of the field.

Suggested Citation

  • Mohammad S. Islam & Shahid Husain & Jawed Mustafa & Yuantong Gu, 2022. "A Novel Machine Learning Prediction Model for Aerosol Transport in Upper 17-Generations of the Human Respiratory Tract," Future Internet, MDPI, vol. 14(9), pages 1-16, August.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:9:p:247-:d:896179
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