Prediction of COVID’19 Through Multiple Organ Analysis Using IoT Devices and Machine Learning Techniques

Prediction of COVID’19 Through Multiple Organ Analysis Using IoT Devices and Machine Learning Techniques
  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-8
Year of Publication : 2021
Authors : T. Jemima Jebaseeli, D. Jasmine David, R. Venkatesan
DOI :  10.14445/22315381/IJETT-V69I8P213

How to Cite?

Imad El Adraoui, Mohammed Bouaicha, Hassan Gziri, Mourad Zegrari, "Prediction of COVID’19 Through Multiple Organ Analysis Using IoT Devices and Machine Learning Techniques," International Journal of Engineering Trends and Technology, vol. 69, no. 8, pp. 102-108, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I8P213

Abstract
COVID-19 is a recently found coronavirus that tends to cause serious infections. It falls under the stage of mild to moderate does not require hospitalization. If the patient`s immune system is strong, they can recover on their own with proper nutrition and treatment. This disease has an impact on the human hormone system. A computer-aided diagnosis is needed to predict COVID-19. The blood volume must be determined in order to predict the disease`s severity level. The blood vessels or capillaries provide oxygen to the Red Blood Cells (RBCs), and the RBCs, in turn, provide oxygen to the internal organs. The wall and lining of the alveolus and capillaries are damaged and thickened by COVID-19. The oxygen transfer by RBCs becomes extremely difficult as the wall thickens. The body has trouble breathing as a result of this condition. This is the most common cause of respiratory problems in COVID-19 patients. Respiratory issues cause problems on the retina, triggering haemorrhages. It also has an impact on the human digestive tract and taste buds. This has been confirmed in medical studies. As a result, of diagnosis, the proposed IoT-based method needs microscopic blood smear images, CT images of the digestive tract, X-ray images of the chest, and fundus images of the eye. Hence, machine learning techniques have been used to process these images and yield more accurate results in diagnosis.

Keywords
Segmentation, classification, feature extraction, X-ray, retina, fundus, lungs, chest, tongue.

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