Journal of Autonomous Intelligence (2023) Volume 6 Issue 3 doi: 10.32629/jai.v6i3.1001 1 Original Research Article Lung pressure predictive model using LSTM: A deep learning techniques Nilesh P. Sable 1,* , Rajkumar V. Patil 2 , Mahendra Deore 3 , Parikshit N. Mahalle 4 , Gitanjali Rahul Shinde 5 , Sunil D. Kale 4 1 Department of Computer Science & Engineering (Artificial Intelligence), Bansilal Ramnath Agarwal Charitable Trust’s, Vishwakarma Institute of Information Technology, Pune 411048, Maharashtra, India 2 MIT Art, Design & Technology University, Pune 412201, Maharashtra, India 3 Department of Computer Engineering, MKSSS’s Cummins College of Engineering for Women, Pune 411052, Maharashtra, India 4 Department of Artificial Intelligence & Data Science, Bansilal Ramnath Agarwal Charitable Trust’s, Vishwakarma Institute of Information Technology, Pune 411048, Maharashtra, India 5 Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning), Bansilal Ramnath Agarwal Charitable Trust’s, Vishwakarma Institute of Information Technology, Pune 411048, Maharashtra, India * Corresponding author: Nilesh P. Sable, drsablenilesh@gmail.com ABSTRACT The human body relies on controlled breathing to ensure oxygen reaches all cells while filtering out contaminants to protect the lungs. However, infections like the Delta virus and SARS-CoV2 (COVID-19) have led to Acute Respiratory Distress Syndrome (ARDS), requiring urgent medical care, including mechanical ventilation. The overwhelming number of patients has strained healthcare organizations and workers, necessitating advancements in automated healthcare technology. To address this challenge, we propose a novel solution to predict pressure in mechanical ventilation (MV) for various lung illnesses. The goal is to accurately predict the pressure within the respiratory circuit, which poses a challenging sequence prediction issue. To tackle this, we employ a cutting-edge deep learning approach known as Long Short-Term Memory (LSTM), which exhibits remarkable performance in selectively recalling patterns over time. While traditional recurrent neural networks (RNNs) can handle short-term patterns well, the introduced LSTM technique excels in managing complex sequence prediction problems. Comparing the proposed method with four existing algorithms, the researchers demonstrate that their approach achieves significantly higher accuracy. The impressively low error rate of 1.85 × 10 −7 showcases a substantial improvement over existing system. This groundbreaking advancement has the potential to alleviate the pressure on the current healthcare infrastructure and significantly improve care for patients in need of mechanical ventilation due to respiratory issues. Keywords: LSTM; deep learning; lung pressure prediction; RNN; COVID-19 1. Introduction The lungs are the main organs that are active in the process of breathing in and out. A human being’s lungs may be found on both the left and right sides of their chest. The size of the left lung is much less than that of the right lung, which frees up more room for the heart. With each breath, the chest goes through a rhythmic expansion and contraction. This is because the lungs expand during inhalation, but contract during exhalation. The reason for this is because the lungs expand during inhalation but contract during exhalation. Bringing oxygen into the circulation is the job of the lungs, which are the organs ARTICLE INFO Received: 18 July 2023 Accepted: 8 August 2023 Available online: 13 September 2023 COPYRIGHT Copyright © 2023 by author(s). Journal of Autonomous Intelligence is published by Frontier Scientific Publishing. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). https://creativecommons.org/licenses/by- nc/4.0/