ABSTRACT
Global public health is overwhelmed due to the ongoing Corona Virus Disease (COVID-19). As of October 2022, the causative virus SARS-CoV-2 and its multiple variants have infected more than 600 million confirmed cases and nearly 6.5 million fatalities globally. The main objective of this reported study is to understand the COVID-19 infection better from the chest X-ray (CXR) image database of COVID-19 cases from the dataset of CXR of normal, pneumonia and COVID-19 patients. Deep learning approaches like VGG-16 and LSTM models were used to classify images as normal, pneumonia and COVID-19 impacted by extracting the features. It has been observed during the COVID-19 pandemic peaks that large number of patients could not avail medical beds and were seen stranded outdoors. To address such health emergency situations with limited available bed and scarcity of expert physicians, computer-aided analysis could save precious lives through early screening and appropriate care. Such computer-based deep-learning strategy could help during future pandemics, especially when the available health resources and the need for preventive measures to take do not match the burden of a disease. [ FROM AUTHOR] Copyright of Journal of Pure & Applied Microbiology is the property of Dr. M. N. Khan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Objective: Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods: Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results: From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion: The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come.