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

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-8
Year of Publication : 2021
Authors : T. Jemima Jebaseeli, D. Jasmine David, R. Venkatesan


MLA Style: T. Jemima Jebaseeli, D. Jasmine David, R. Venkatesan "Prediction of COVID’19 Through Multiple Organ Analysis Using IoT Devices and Machine Learning Techniques" International Journal of Engineering Trends and Technology 69.8(2021):102-108. 

APA Style: T. Jemima Jebaseeli, D. Jasmine David, R. Venkatesan. Prediction of COVID’19 Through Multiple Organ Analysis Using IoT Devices and Machine Learning Techniques International Journal of Engineering Trends and Technology, 69(8),102-108.

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.

[1] Maryam Baghizadeh Fini., Oral Saliva and COVID-19, Oral Oncology, 108 (2020).
[2] Daniela Bacherini, Ilaria Biagini, Chiara Lenzetti, Gianni Virgili, & Stanislao Rizzo Fabrizio Giansanti., The COVID-19 Pandemic from an Ophthalmologist’s Perspective, Trends of Molecular Medicine, Cell Press Reviews, 26(6) (2020) 529-531.
[3] Melika Lotfi, Michael R.Hamblin, Nima Rezaei, & Clinica Chimica Acta., COVID-19: Transmission, prevention, and potential therapeutic opportunities, 508 (2020) 254-266.
[4] Lan Dong, Jinhua Tian, & Songming He., Possible Vertical Transmission of SARS-CoV-2 From an Infected Mother to Her Newborn, JAMA, 323(18) (2020) 1846-1848.
[5] Hui Zeng, Chen Xu BS, & Junli Fan., Antibodies in Infants Born to Mothers With COVID-19 Pneumonia, JAMA, 323(18) (2020) 1848-1849.
[6] The second dog in Hong Kong tests positive for coronavirus. (2020). straitstimes T. second-dog-in-hong-kong-tests-positive-for-coronavirus.
[7] Carlos del Rio, Preeti N, & Malani., COVID-19—New Insights on a Rapidly Changing Epidemic, JAMA, 323(14) (2020) 1339- 1340.
[8] W.Yang, Q. Cao, & L. Qin., Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID- 19): a multi-center study in Wenzhou city, Zhejiang, China, Journal of Infection, (2020).
[10] Feng-YanZhang, YingQiao, HuiZhang, CT imaging of the COVID-19, Journal of the Formosan Medical Association, (2020).
[11] M.Schmulson, M.F.Davalos, J.Berumen, Beware: Gastrointestinal symptoms can be a manifestation of COVID-19, Revista de Gastroenterología de Mexico, (2020).
[12] XiaorongWanga, YayaZhoua, Nanchuan Jiang, Qiong Zhou, Wan-LiMa, Persistence of intestinal SARS-CoV-2 infection in patients with COVID-19 leads to re-admission after pneumonia resolved, International Journal of Infectious Diseases, 95 (2020) 433-435.
[13] Mohammad Waheed El-Anwar, Saad Elzayat, Yasser Ahmed Fouad, ENT manifestation in COVID-19 patients, Auris Nasus Larynx, (2020).
[14] YijinWang, huhongLiu, HongyangLiu, WeiLi, FangLin, LinaJiang, XiLi, Pengfei Xu, Lixin Zhang, LihuaZhao, YunCao, JiaruiKang, JianfaYang, LingLi, Xiaoyan Liu, YanLi, RuifangNie, Jinsong Mu, Jingmin Zhao, SARS-CoV-2 infection of the liver directly contributes to hepatic impairment in patients with COVID-19, Journal of Hepatology, (2020).
[15] Carl Philpott., Coronavirus: Loss of smell and taste reported as early symptoms of COVID-19, (2020). smell-and-taste-reported-as-early-symptoms-of-covid-19.html
[16] M.Gornet, M.L.Tran Minh, F.Leleu, & D.Hassid., What do surgeons need to know about the digestive disorders and paraclinical abnormalities induced by COVID-19?, Journal of Visceral Surger, (2020).
[17] Harsh Panwar, P.K.Gupta, Mohammad Khubeb Siddiqui, Ruben Morales-Menendez, & Vaishnavi Singha, Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet, Chaos, Solitons & Fractals, 138 (2020).
[18] Giulia Corradetti, Federico Corvi, Tieu VyNguyen, & SriniVas R.Sadda., Management of neovascular age-related macular degeneration during the COVID-19 pandemia, Ophthalmology Retina, (2020).
[19] Yueyang Zhong, Kai Wang, Yanan Zhu, Danni Lyu, & KeYao., COVID-19 and the Eye, Journal of Infection, (2020).
[20] nttypeid=134&contentid=239
[21] voice/
[22] Does-COVID-19- infect-peripheral-blood-cells.aspx
[23] Hiroshi Nishiura, Natalie M. Linton, & Andrei R. Akhmetzhanov., Serial interval of novel coronavirus (COVID-19) infections, International Journal of Infectious Diseases, 93 (2020) 284–286.
[24] Ahmed Hamimi., MERS-CoV: the Middle East respiratory syndrome coronavirus: Can radiology be of help? Initial singlecenter experience, The Egyptian Journal of Radiology and Nuclear Medicine, 47 (2016) 95–106.
[25] Ajlan AM, Ahyad RA, & Jamjoom LG., Middle East respiratory syndrome coronavirus (MERS-CoV) infection: chest CT findings, AJR, 203(4) (2014).
[26] WHO Coronavirus Disease (COVID-19) Dashboard. gclid=EAIaIQobChMIxIaxlLSY6gIV1wRyCh16ZgLkEAAYASA AEgLGpfD_BwE

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