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The IOMT-Based Risk-Free Approach to Lung Disorders Detection from Exhaled Breath Examination

Mohsin Ghani, Ghulam Gilanie*

Department of Artificial Intelligence, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan

* Corresponding Author: Ghulam Gilanie. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 2835-2847. https://doi.org/10.32604/iasc.2023.034857

Abstract

The lungs are the main fundamental part of the human respiratory system and are among the major organs of the human body. Lung disorders, including Coronavirus (Covid-19), are among the world’s deadliest and most life-threatening diseases. Early and social distance-based detection and treatment can save lives as well as protect the rest of humanity. Even though X-rays or Computed Tomography (CT) scans are the imaging techniques to analyze lung-related disorders, medical practitioners still find it challenging to analyze and identify lung cancer from scanned images. unless COVID-19 reaches the lungs, it is unable to be diagnosed. through these modalities. So, the Internet of Medical Things (IoMT) and machine learning-based computer-assisted approaches have been developed and applied to automate these diagnostic procedures. This study also aims at investigating an automated approach for the detection of COVID-19 and lung disorders other than COVID-19 infection in a non-invasive manner at their early stages through the analysis of human breath. Human breath contains several volatile organic compounds, i.e., water vapor (5.0%–6.3%), nitrogen (79%), oxygen (13.6%–16.0%), carbon dioxide (4.0%–5.3%), argon (1%), hydrogen (1 ppm) (parts per million), carbon monoxide (1%), proteins (1%), isoprene (1%), acetone (1%), and ammonia (1%). Beyond these limits, the presence of a certain volatile organic compound (VOC) may indicate a disease. The proposed research not only aims to increase the accuracy of lung disorder detection from breath analysis but also to deploy the model in a real-time environment as a home appliance. Different sensors detect VOC; microcontrollers and machine learning models have been used to detect these lung disorders. Overall, the suggested methodology is accurate, efficient, and non-invasive. The proposed method obtained an accuracy of 93.59%, a sensitivity of 89.59%, a specificity of 94.87%, and an AUC-Value of 0.96.

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APA Style
Ghani, M., Gilanie, G. (2023). The iomt-based risk-free approach to lung disorders detection from exhaled breath examination. Intelligent Automation & Soft Computing, 36(3), 2835-2847. https://doi.org/10.32604/iasc.2023.034857
Vancouver Style
Ghani M, Gilanie G. The iomt-based risk-free approach to lung disorders detection from exhaled breath examination. Intell Automat Soft Comput . 2023;36(3):2835-2847 https://doi.org/10.32604/iasc.2023.034857
IEEE Style
M. Ghani and G. Gilanie, "The IOMT-Based Risk-Free Approach to Lung Disorders Detection from Exhaled Breath Examination," Intell. Automat. Soft Comput. , vol. 36, no. 3, pp. 2835-2847. 2023. https://doi.org/10.32604/iasc.2023.034857



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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