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. © 2023, Tech Science Press. All rights reserved.
ABSTRACT
The COVID-19 outbreak is moving individuals globally. Monitoring social media and internet news is now vital to grasping this phenomenon and its impact. This study’s goal is to offer a method for capturing important concepts and themes addressed in the mainstream media and social networks and then apply it to the COVID-19 outbreak. This study compares articles and news, then visualizes the evolution and influence of the COVID-19 epidemic articles. The COVID-19 articles dataset from Kaggle is utilized for experiments. Various articles use the dataset to examine COVID-19 reviews. The studies use datasets and models such as Decision Tree (DT), Logistic Regression (LR), and Extra Tree Classifier (ETC), with F1 score, precision, and recall being evaluated. To improve accuracy, Term Frequency-Inverse Document Frequency (TF-IDF) is applied to the extracted keywords. The assembled model enhances this research, and the Logistic Regression (LR) model provides the highest accuracy at 90%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.