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-th...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%),hydro-gen(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.展开更多
文摘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%),hydro-gen(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.
