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An Effective Diagnostic Framework for COVID-19 Using an Integrated Approach

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Next Generation of Internet of Things

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 445))

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Abstract

The coronavirus, one of the deadliest virus erupted in Wuhan, China in December and has claimed millions of lives worldwide and infected too. This virus has off-late demonstrated mutations thus making it difficult for the health professionals to adopt a uniform means of cure. Many people due to lack of support have confined themselves at home. The hospitals too are running short of equipment and support systems. Thus, computational connectivity between the patients at home and the hospitals needs to be established. The objective of this paper is to propose a framework/model that connects all the stakeholders so that either in regular monitoring or in emergency cases help can be provided to them. It has been well established through research and case studies that critical factors associated with this disease are oxygen level (SPO2), pulse rate, fever, chest infection, cough causing choking, and breathlessness. Data shall be collected, stored, and analyzed for the above symptoms and for this cloud storage and blockchain technology would be used. It has been established through various studies that non-clinical techniques like AI and machine learning prove to be effective for the prediction and diagnosis of COVID-19. Using this theory as the standard basis, machine learning models like SVM, Naïve Bayes, and decision trees can be used for the analysis, diagnosis, and prediction. Using IoT and its variants, remote monitoring of patient, and consultation can be provided to the patient. Appropriate action would be taken. In addition, a mobile application would enable the patients to gather or read about experiences of other patients. Thus, it would be established through the proposed framework, that an integrated approach of technologies has a great potential in such applications and offers several advantages.

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References

  1. Tumpey T, García-Sastre A, Taubenberger J, Palese P, Swayne D, Pantin-Jackwood M, Schultz-Cherry S, Solórzano A, Van Rooijen N, Katz J, Basler C (2005) Pathogenicity of influenza viruses with genes from the 1918 pandemic virus: functional roles of alveolar macrophages and neutrophils in limiting virus replication and mortality in mice. J Virol 79:14933–14944. https://doi.org/10.1128/JVI.79.23.14933-14944.2005

  2. Erkoreka A (2010) The Spanish influenza pandemic in occidental Europe (1918–1920) and victim age. Influenza Other Respir Viruses 4:81–89

    Google Scholar 

  3. Xu Y, Li X, Zhu B, Liang H, Fang C, Gong Y, Guo Q, Sun X, Zhao D, Shen J, Zhang H, Liu H, Xia H, Tang J, Zhang K, Gong S (2020) Characteristics of pediatric SARS-CoV-2 infection and potential evidence for persistent fecal viral shedding. Nat Med 26(4):502–505. https://doi.org/10.1111/j.1750-2659.2009.00125.x

  4. World Health Organization (2020) ‘WHO Director-General's opening remarks at the media briefing on COVID-19–11 March 2020. https://www.who.int/dg/speeches/detail/whodirector-general-s-opening-remarks-at-the-media-briefingon-covid-19-11-march-2020

  5. Rothan HA, Byrareddy SN (2020) The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun 109:102433. https://doi.org/10.1016/j.jaut.2020.102433

  6. Liu K, Chen Y, Lin R, Han K (2020) Clinical features of COVID-19 in elderly patients: a comparison with young and middle-aged patients. J infect 80(6):e14–e18

    Google Scholar 

  7. Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthcare J 6(2):94–98. https://doi.org/10.7861/futurehosp.6-2-94

  8. Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R (2020) World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg 76:71–76. https://doi.org/10.1016/j.ijsu.2020.02.034

  9. Chen DS, Yang J, Yang W, Wang C, Bärnighausen T (2020) COVID-19 control in China during mass population movements at New Year. Lancet 395(10226):764–766

    Google Scholar 

  10. Bobdey S, Ray S (2020) Going viral–COVID-19 impact assessment a perspective beyond clinical practice. J Mar Med Soc 22(1):9

    Google Scholar 

  11. Haleem A, Vaishya R, Javaid M, Khan IH (2020) Artificial Intelligence (AI) applications in orthopaedics: an innovative technology to embrace. J Clin Orthop Trauma 11(Suppl 1):S80–S81.https://doi.org/10.1016/j.jcot.2019.06.012

  12. Biswas K, Sen P (2020) Space-time dependence of coronavirus (COVID-19) outbreak. arXiv preprint arXiv:2003.03149. Mar 6

  13. Stebbing J, Phelan A, Griffin I, Tucker C, Oechsle O, Smith D, Richardson P (2020) COVID-19 Combining antiviral and anti-inflammatory treatments. The Lancet. Infect Dis 20(4):400–402.https://doi.org/10.1016/S1473-3099(20)30132-8

  14. Hu Z, Ge Q, Jin L, Xiong M (2020) Artificial intelligence forecasting of COVID-19 in China. arXiv preprint arXiv:2002.07112. Feb 17

  15. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L (2020) Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2):E32–E40. https://doi.org/10.1148/radiol.2020200642

  16. Luo H, Tang QL, Shang YX, Liang SB, Yang M, Robinson N, Liu JP (2020) Can Chinese medicine be used for prevention of corona virus disease 2019 (COVID-19)? A review of historical classics, research evidence and current prevention programs. Chin J Integr Med 26(4):243–250. https://doi.org/10.1007/s11655-020-3192-6

  17. Agrebi S, Larbi A (2020).Use of artificial intelligence in infectious diseases. A I Prec Health 415–438.https://doi.org/10.1016/B978-0-12-817133-2.00018-5

  18. Tuli S, Tuli S, Wander G, Gill P, Singh D, Schahram S, Rizos R, Omer (2019) Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions. Internet Technol Lett 3:e145. https://doi.org/10.1002/itl2.145

  19. Bai Y, Jin Z (2005) Prediction of SARS epidemic by BP neural networks with online prediction strategy. Chaos, Solitons Fractals 26(2):559–569

    Google Scholar 

  20. Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M (2020) Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief 29:105340. https://doi.org/10.1016/j.dib.2020.105340

  21. Chimmula VKR, Zhang L (2020) Time series forecasting of COVID-19 transmission in Canada using LSTM Networks. Chaos, Solitons Fractals. https://doi.org/10.1016/S0140-6736(20)30421-9

  22. Pirouz B, Shaffiee HS, Piro P (2020) Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability 12:2427

    Google Scholar 

  23. Gozes O, Frid AM, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E (2020) Rapid AI development cycle for the Coronavirus (COVID-19) pandemic: initial results for automated detection & patient monitoring using deep learning CT image analysis. arXiv preprint arXiv:2003.05037. Mar 10

  24. Wang S, Kang B, Ma J et al (2020) A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv. https://doi.org/10.1101/2020.02.14.20023028

  25. Wu F, Wu T, Yuce MR (2018) An Internet-of-Things (IoT) network system for connected safety and health monitoring applications. Sensors 19(1):21. https://doi.org/10.3390/s19010021

  26. Annis T, Pleasants S, Hultman G, Lindemann E, Thompson JA, Billecke S, Badlani S, Melton GB (2020) Rapid implementation of a COVID-19 remote patient monitoring program. J Am Med Inform Assoc 27(8):1326–1330. https://doi.org/10.1093/jamia/ocaa097

  27. BBC: Coronavirus: Israel enables emergency spy powers. https://www.bbc.com/news/technology-51930681

  28. MIT: Covid Tracing Tracker - a flood of coronavirus apps are tracking us. Now it's time to keep track of them. https://www.technologyreview.com/2020/05/07/1000961/launching-mittr-covid-tracing-tracker/

  29. Ekins S, Mottin M, Ramos PRPS, Sousa BKP, Neves BJ, Foil DH, Zorn KM, Braga RC, Coffee M, Southan C, Puhl AC, Andrade CH (2020) Déjà vu: stimulating open drug discovery for SARS-CoV-2. Drug Discovery Today 25(5):928–941

    Google Scholar 

  30. Maghdid HS, Ghafoor KZ, Sadiq AS, Curran K, Rabie K (2020) A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: design study. arXiv preprint: 2003.07434

    Google Scholar 

  31. Kalla A, Hewa T, Mishra RA, Ylianttila M, Liyanage M (2020) The role of blockchain to fight against COVID-19. IEEE Eng Manage Rev 48(3). https://doi.org/10.1109/EMR.2020.3014052

  32. Ting D, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 26(4):459–461. https://doi.org/10.1038/s41591-020-0824-5

  33. Wan KH, Huang SS, Young AL, Lam D (2020) Precautionary measures needed for ophthalmologists during pandemic of the coronavirus disease 2019 (COVID-19). Acta Ophthalmol 98(3):221–222. https://doi.org/10.1111/aos.14438

  34. Nagendran M, Chen Y, Lovejoy CA, Gordon A, C, Komorowski M, Harvey H, Topol EJ, Ioannidis JPA, Collins GS, Maruthappu M (2020) Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies in medical imaging. BMJ 368:1–12

    Google Scholar 

  35. Kumar P, Kumari S (2020) Detection of coronavirus disease (COVID-19) based on deep features. https://www.Preprints.Org/Manuscript/202003.0300/V1. March, p 9

  36. Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43(2):635–640. https://doi.org/10.1007/s13246-020-00865-4

  37. Usak M, Kubiatko M, Shabbir MS, Dudnik OV, Jermsittiparsert K, Rajabion L (2020) Health care service delivery based on the internet of things: a systematic and comprehensive study. Int J Commun Syst 33(2):e4179. https://doi.org/10.1002/dac.4179

  38. Wu J, Zhang P, Zhang L, Meng W, Li J, Tong C, Li Y, Cai J, Yang Z, JZhu J, Zhao M, Huang H, Xie X, Li S (2020) Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results. https://doi.org/10.1101/2020.04.02.20051136

  39. Hamidi H (2019) An approach to develop the smart health using internet of things and authentication based on biometric technology. Future Gener Comput Syst 91:434–449

    Google Scholar 

  40. Rath M, Pattanayak B (2019) Technological improvement in modern health care applications using Internet of Things (IoT) and proposal of novel health care approach. Int J Hum Rights Healthcare 12(2):148–162. https://doi.org/10.1108/IJHRH-01-2018-0007

  41. Darwish A, Hassanien A, Elhoseny M, Sangaiah AK, Muhammad K (2019) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Humaniz Comput 10:4151–4166

    Google Scholar 

  42. Zhong CL, Li YL (2020) Internet of things sensors assisted physical activity recognition and health monitoring of college students. Measurement 159

    Google Scholar 

  43. Din S, Paul A (2019) Smart health monitoring and management system: toward autonomous wearable sensing for Internet of Things using big data analytics. Futur Gener Comput Syst 91:11–619. https://doi.org/10.1016/j.future.2017.12.059

  44. Thi NT (2020) Artificial Intelligence in the battle against coronavirus (COVID-19): a survey and future research directions. figshare. Preprint. https://doi.org/10.6084/m9.figshare.12127020.v6

  45. Rao SA, Vazquez JA (2020) Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine. Infect Control Hosp Epidemiol 41(7):826–830. https://doi.org/10.1017/ice.2020.61

  46. Yang Z, Zeng Z, Wang K, Wong SS, Liang W, Zanin M, Liu P, Cao X, Gao Z, Mai Z, Liang J, Liu X, Li S, Li Y, Ye F, Guan W, Yang Y, Li F, Luo S, Xie Y, He J (2020) Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thoracic Dis 12(3):165–174 https://doi.org/10.21037/jtd.2020.02.64

  47. Zhavoronkov A, Aladinskiy V, Zhebrak A, Zagribelnyy B, Terentiev V, Bezrukov DS et al (2020) Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.11829102.v2

  48. Xiaowei X, Xiangao J, Chunlian M, Peng D, Xukun L, Shuangzhi L, Liang Y, Qin N, Yanfei C, Junwei S, Guanjing L, Yongtao L, Hong Z, Jun L, Kaijin X, Lingxiang R, Jifang S, Yunqing Q, Wei W, Tingbo L, Lanjuan L (2019) A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineeringhttps://doi.org/10.1016/j.eng.2020.04.010

  49. Hemdan EED, Shouman MA, Karar ME (2020) COVIDX-Net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. https://arxiv.org/abs/2003.11055

  50. Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (COVID-19) Classification using CT Images by machine learning methods 5:1–10. Arxiv: arXiv:2003.09424

  51. Medina J, Espinilla M, Fernández ALG, Martínez L, Intelligent multi-dose medication controller for fever: from wearable devices to remote dispensers. Comput Electr Eng 65:400–412. https://doi.org/10.1016/j.compeleceng.2017.03.012

  52. Umayahara Y, Soh Z, Sekikawa K, Kawae T, Otsuka A, Tsuji T (2018) A mobile cough strength evaluation device using cough sounds. Sensors 18(11):3810. https://doi.org/10.3390/s18113810

  53. Ichwana D, Ikhlas RZ, Ekariani S (2018) Heart rate monitoring system during physical exercise for fatigue warning using non-invasive wearable sensor. In: 2018 International conference on information technology systems and innovation (ICTSI), Bandung - Padang, Indonesia, pp 497–502

    Google Scholar 

  54. Askarian B, Yoo SC, Chong JW (2019) Novel image processing method for detecting strep throat (streptococcal pharyngitis) using smartphone. Sensors 19(15):3307. https://doi.org/10.3390/s19153307

  55. Gaidhani A, Moon KS, Ozturk Y, Lee SQ, Youm W (2017) Extraction and analysis of respiratory motion using wearable inertial sensor system during trunk motion. Sensors 17(12):2932. https://doi.org/10.3390/s17122932

  56. Fan K, Wang S, Ren Y, Li H, Yang Y (2018) MedBlock: efficient and secure medical data sharing via blockchain. J Med Syst 42(8):136. https://doi.org/10.1007/s10916-018-0993-7

  57. Li Y, Hai T, Z, Jeorge G et al (2020). A machine learning-based model for survival prediction in patients with severe COVID-19 infection medRxiv 2020.02.27.20028027. https://doi.org/10.1101/2020.02.27.20028027

  58. Muhammad LJ, Algehyne EA, Usman SS et al (2021) Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN Comput Sci 2:11. https://doi.org/10.1007/s42979-020-00394-7

  59. Muhammad LJ, Islam MM, Usman SS et al (2020) Predictive data mining models for novel coronavirus (COVID-19) infected patients’ recovery. SN Comput Sci 1:206. https://doi.org/10.1007/s42979-020-00216-w

  60. Chew AMK, Ryan O, Hsien LH, Mallika R, Grisan KV, Verma SK, Fung DSS, Sheng S, Leong JJ, Gunasekeran DV (2020) Digital health solutions for mental health disorders during COVID-19. Front Psychiatry 11:898.https://doi.org/10.3389/fpsyt.2020.582007

  61. Shahzad F, Abid F, Obaid A, Kumar Rai B, Ashraf M, Abdulbaqi A (2021) Forward stepwise logistic regression approach for determinants of hepatitis B & C among Hiv/Aids patients. Int J Nonlinear Anal Appl 12(Special Issue):1367–1396. https://doi.org/10.22075/ijnaa.2022.5717

  62. Kautish S, Peng S-L, Obaid AJ (2021) Computational intelligence techniques for combating COVID-19. Springer International Publishing

    Google Scholar 

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Authors and Affiliations

  1. Jamia Hamdard, New Delhi, India

    Parul Agarwal

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Sheikh Mohammad Idrees

  3. Faculty of Computer Science and Mathematics, University of Kufa, Kufa, Iraq

    Ahmed J. Obaid

  4. College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

    Azmi Shawkat Abdulbaqi & Sawsan Dheyaa Mahmood

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  1. Parul Agarwal
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  2. Sheikh Mohammad Idrees
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  3. Ahmed J. Obaid
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  4. Azmi Shawkat Abdulbaqi
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  5. Sawsan Dheyaa Mahmood
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Correspondence to Parul Agarwal .

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Editors and Affiliations

  1. GIET University, Gunupur, India

    Raghvendra Kumar

  2. KIIT University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

  3. Faculdade de Engenharia da, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Agarwal, P., Idrees, S.M., Obaid, A.J., Abdulbaqi, A.S., Mahmood, S.D. (2023). An Effective Diagnostic Framework for COVID-19 Using an Integrated Approach. In: Kumar, R., Pattnaik, P.K., R. S. Tavares, J.M. (eds) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol 445. Springer, Singapore. https://doi.org/10.1007/978-981-19-1412-6_11

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