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JSDP 2023, 20(3): 87-102 |
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Hosseini S Z, Radfar R, Nasiripour A, Rajabzadeh Ghatary A. Designing an optimal diagnosis algorithm based on IoT for Covid-19. JSDP 2023; 20 (3) : 7
URL: http://jsdp.rcisp.ac.ir/article-1-1292-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1292-en.html
Islamic Azad University
Abstract: (790 Views)
The development of information technology and its use in the health system has taken many measures to protect and promote human health, however, the world still faces long-term threats and recurrence of infectious diseases.
Understanding the dynamics of infectious diseases is important in controlling the disease because the network and the mode of impact of infectious diseases are very complex. The management of infectious diseases can also be considered as a complex social system due to the fact that has many complexities (such as dimensions, parameters, interactions, behaviors and rules), for this reason, the approach of the present study is a multifaceted understanding of the spread of infectious diseases. To design the present model, an intelligent system with a combination of mathematical, machine learning and epidemiological dimensions is proposed.
The disease studied in this study, due to its importance and prevalence, is Covid 19.
In this study, with the approach of complex systems and using the Internet of Things and machine learning methods, an algorithm was presented that uses environmental and individual variables to predict the probability of disease in an individual. Therefore, this research can improve the prevention of infectious diseases by filling some of the gaps in 3 sections: 1- Re-emergence of infectious diseases and the potential of IoT and AI, 2- Speed of dissemination and importance of real-time tracking, and 3- Budget and cost.
The evaluation of the algorithm in this study was determined by two criteria of sensitivity and specificity.
The results of the proposed algorithm for predicting Covid 19 disease showed an accuracy of more than 98%. Sensitivity above 98% was also obtained. Which is very important for the diagnosis of Covid disease 19 and shows the low number of false negatives in the test results.
Therefore, the proposed model, combined with the Internet of Things and machine learning, can cause early diagnosis and prevent the spread of the Covid-19 disease with high specificity and sensitivity.
Understanding the dynamics of infectious diseases is important in controlling the disease because the network and the mode of impact of infectious diseases are very complex. The management of infectious diseases can also be considered as a complex social system due to the fact that has many complexities (such as dimensions, parameters, interactions, behaviors and rules), for this reason, the approach of the present study is a multifaceted understanding of the spread of infectious diseases. To design the present model, an intelligent system with a combination of mathematical, machine learning and epidemiological dimensions is proposed.
The disease studied in this study, due to its importance and prevalence, is Covid 19.
In this study, with the approach of complex systems and using the Internet of Things and machine learning methods, an algorithm was presented that uses environmental and individual variables to predict the probability of disease in an individual. Therefore, this research can improve the prevention of infectious diseases by filling some of the gaps in 3 sections: 1- Re-emergence of infectious diseases and the potential of IoT and AI, 2- Speed of dissemination and importance of real-time tracking, and 3- Budget and cost.
The evaluation of the algorithm in this study was determined by two criteria of sensitivity and specificity.
The results of the proposed algorithm for predicting Covid 19 disease showed an accuracy of more than 98%. Sensitivity above 98% was also obtained. Which is very important for the diagnosis of Covid disease 19 and shows the low number of false negatives in the test results.
Therefore, the proposed model, combined with the Internet of Things and machine learning, can cause early diagnosis and prevent the spread of the Covid-19 disease with high specificity and sensitivity.
Article number: 7
Type of Study: Applicable |
Subject:
Paper
Received: 2022/01/21 | Accepted: 2023/06/2 | Published: 2024/01/14 | ePublished: 2024/01/14
Received: 2022/01/21 | Accepted: 2023/06/2 | Published: 2024/01/14 | ePublished: 2024/01/14
References
1. ] 1[ N. A. o. Sciences, Informing the Critical Issues in Health Future:, National Academy of Sciences, 2003.
2. ] 2[ WHO, Managing epidemics: key facts about major deadly diseases, Luxembourg: World Health Organization, 2018.
3. [3] مرکزکشوری مدیریت سلامت، "احتیاطات در کنترل عفونت بیماریهای واگیر," دانشگاه علوم پزشکی و خدمات بهداشتی درمانی تبریز, تبریز, 1395.
4. ] 4[ P. Edwards, "Epidemics: past, present and future -what are the risks?," ReCent medical news, 2017.
5. ] 5[ J. Astill, R. Dara, E. Fraser و S. Shayan, "Detecting and predicting emerging disease in poultry with the implementation of new technologies and big data," Frontiers in Veterinary Science, pp. 1-43, 2018.
6. ] 6[ S. Madanian, D. T. Parry, D. Airehrour و M. Cherrington, "mHealth and big-data integration: promises for healthcare system in India," BMJ Health Care Inform, pp. 1-8, 2020.
7. ] 7[ V. V. Khanna, K. Chadaga, N. Sampathila, S. Prabhu, R. Chadaga و S. Umakanth , "Diagnosing COVID-19 using artificial intelligence: a comprehensive review," Network Modeling Analysis in Health Informatics and Bioinformatics, pp. 1-32, 2022.
8. ] 8[ V. Chamola, V. Hassija, V. Gupta و M. Guizani, "A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, COVID-19 Pandemic and the Role of IoT, Managing its Impact," IEEE Access, pp. 1-35, 2020.
9. ] 9[ م. نعمتشاهی, ح. ابراهیمیپور, ز. کیوانلو, م. خواجه دلویی و ع. کیخسروی, "بررسی بودجه بخش بهداشت و درمان طی برنامههای اول تا پنجم توسعه اجتماعی اقتصادی کشور," راهبردهای مدیریت در نظام سلامت, pp. 85-87, 1399.
10. [10] ر. گلپیرا, م. قطبی, ف. بهتاج, م. پروان, ف. لطفی گلمیشه و ا. واحدی, "شناسنامه شاخصهای آمار و اطلاعات بیمارستانی," معاونت درمان وزارت بهداشت، درمان و آموزش پزشکی, تهران, 1399.
11. ] 11[ J. Phua, M. Farug, A. Kulkarni و I. Redjeki, "Critical Care Bed Capacity in Asian Countries and Regions," Critical Care Medicine, pp. 1-10, 2020.
12. [12] ع. عبادی, "کمتر از یک پرستار به ازای هر تخت بیمارستانی," خبرگزاری تسنیم, تهران, 1400.
13. ] 13[ E. S. Berner, "History of Health Information Technology in the U.S.," در Health IT Workforce Curriculum, Alabama, National Coordinator for Health Information Technology, 2017, pp. 1-77.
14. ] 14[ C. Chakraborty, A. Banerjee, L. Garg, J. J. P. و C. Rodrigues, Internet of Medical Things for Smart Healthcare, Singapore: registered company Springer Nature Singapore, 2021.
15. [15] ب. عزیزی و ع. سیفی, "بررسی شرایط مهار بیماری کرونا بر اساس مدل پویایی شناسی همهگیری آن در ایران," مجله علوم پزشکی رازی, pp. 115-128, 1399.
16. ] 16[ Z. Altintas, Biosensors and Nanotechnology: Applications in Health Care Diagnostics, Berlin: John Wiley & Sons, Inc., 2018.
17. [17] س. حسننژاددیوکلائی, "بررسی سیستمهای دینامیکی بعضی مدلهای ریاضی در بیماریهای واگیردار و تجزیه و تحلیل آنها," دانشکده فیزیک دانشگاه تهران, pp. 1-27, 1397.
18. [18] پ. اولیاء, ف. بحرینی, م. ب. افتخاری, م. قانع و آ. فروزان, "تعیین اولویتهای تحقیقاتی سلامت در ایران," مجله دانشکده بهداشت و انستیتو تحقیقات بهداشتی, pp. 9-20, 1390.
19. ] 19[ WHO, Sex, gender and influenza, Geneva: World Health Organization, 2020.
20. [20] م. طباطبایی, اصول پیشگیری و مراقبت از بیماریها, تهران: انتشارات روح قلم, 1395.
21. ] 21[ e. Massaro, D. Kondor و c. Ratti, "Assessing the interplay between human mobility and mosquito borne diseases in urban environments," Scientific RepoRtS, pp. 1-13, 2019.
22. ] 22[ H. Zhu, P. Podesva, X. Liu, H. Zhang, T. Teply, Y. Xu, H. Chang, A. Qian, Y. Lei, Y. Li, A. Niculescu, C. Iliescu و P. Neuzil, "IoT PCR for pandemic disease detection and its spread monitoring," Sensors & Actuators: B. Chemical, pp. 1-7, 2020.
23. ] 23[ Á. M. Ramos, B. Ivorra و B. M. López, Mathematical models for introduction, spread and early detection on infectious diseases in veterinary epidemiology, Madrid: UNIVERSIDAD COMPLUTENSE DE MADRID, 2018.
24. ] 24[ S. Agrebi و A. Larbi, "Use of artificial intelligence in infectious diseases," در Artificial Intelligence in Precision Health, Singapore, Technopark El Gazala, 2020, pp. 415-532.
25. [25] ع. رجب زاده قطری, س. حجتی نیک قدم و م. فریدماسوله, درآمدي بر پژوهش علم طراحي و فراتحليل, تهران: انتشارات نگاه دانش, 1393.
26. ] 26[ S. Bin, G. Sun و C.-C. Chen, "Spread of Infectious Disease Modeling and Analysis of Different Factors on Spread of Infectious Disease Based on Cellular Automata," International Journal of Environmental Research and Public Health, pp. 1-16, 2019.
27. [27] ف. ا. احمدی, خ. نصیریانی و پ. اباذری, "تکنیک دلفی: ابزاری در تحقیق," آموزش در علوم پزشکی, pp. 175-186, 1387.
28. ] 28[ K. Farrahi, R. Emonet و M. Cebrian, "Predicting a Community's Flu Dynamics with Mobile Phone Data," HAL, pp. 1-9, 2015.
29. ] 29[ S. A. Alanazi, M. M. Kamruzzaman, M. Alruwaili, N. Alshammari, S. A. Alqahtani و A. Karime, "Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care," Journal of Healthcare Engineering, pp. 1-12, 2020.
30. ] 30[ M. L. 'opez, A. Peinado و A. '. Ortiz, "An Extensive Validation of a SIR Epidemic Model to Study the Propagation of Jamming Attacks against IoT Wireless Networks," Computer Networks, pp. 1-37, 2019.
31. ] 31[ P. L. Z. L. Y. Z. S. L. Zhifang Liao, "TW SIR: time window based SIR for COVID 19 forecasts," Scientiic Reports, pp. 1-15, 2020.
32. [32] م. بسکابادی و م. دوست پرست, "مدلبندی و دادهکاری دادههای جهانی بیماران ویروس کووید19," طب اورژانس ایران, جلد 7, p. 40, 1399.
33. ] 33[ Tahmid Rashid و D. Wang, "CovidSens: a vision on reliable social sensing for COVID 19," Artificial Intelligence Review, pp. 1-25, 2020.
34. [34] ا. بخشی, م. اصلانی و پ. عابدی, "مروری بر چالشهای نمونهگیری و تشخیص آزمایشگاهی بیماری کووید19," مطالعات علوم پزشکی, pp. 157-178, 1400.
35. ] 35[ "The Medical Internet of Things (MIoT)," Cyient Europe Ltd., London, 2017.
36. ] 36[ H. H. Thary و K. A. Zidan, "A Framework Questionnaire for Diagnosing Infectious Disease Using Machine Learning Techniques," Materials Science and Engineering, pp. 130-148, 2021.
37. ] 37[ A. Panesar, Machine Learning and AI for Healthcare, Coventry: Library of Congress, 2020.
38. ] 38[ R. Alfred و . J. H. Obit, "The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review," machineintelligencespace, pp. 1-12, 2021.
39. ] 39[ S. V.Stehman, "Selecting and interpreting measures of thematic classification accuracy," Remote Sensing of Environment, pp. 77-89, 1997.
40. ] 40[ J. Shreffler و M. R. Huecker, "Diagnostic Testing Accuracy: Sensitivity, Specificity, Predictive Values and Likelihood Ratios," StatPearls, pp. 1-8, 2021.
41. ] 41[ R. Parikh, A. Mathai, S. Parikh, G. C. Sekhar و R. Thomas, "Understanding and using sensitivity, specificity and predictive values," Indian J Ophthalmol, pp. 45-50, 2008.
42. ] 42[ W. Yang, J. Zhang و R. Ma, "The Prediction of Infectious Diseases: A Bibliometric Analysis," International Journal of Environmental Research and Public Health, pp. 1-19, 2020.
43. [43] س. روشن, ن. یعقوبی و ا. مومنی, "کاربست هوش مصنوعی در بخش دولتی," فصلنامه علوم مدیریت ایران, pp. 117-145, 1400.
44. ] 44[ M. Balde, "Fitting SIR model to COVID-19 pandemic data and comparative forcasting with machin learning," SIR Model, pp. 1-20, 2020.
45. ] 45[ M. P. Cheng, J. Papenburg, M. Desjardins, S. Kanjilal, C. Quach, M. Libman, S. Dittrich و C. P. Yansouni, "Diagnostic Testing for Severe Acute Respiratory Syndrome-Related Coronavirus 2: A Narrative Review," Ann Intern Med, pp. 724-734, 2020.
46. [46] م. کلاهدوز, ع. طبیبزاده, م. طاهریزاده, آ. لعلی, م. خوانساری, ح. اژدرکش, ف. صفرنژادتمشکل, م. فعال, م. پناهی و م. کربلایی نیا, "مروری بر کروناویروس 19: کنترل و پیشگیری," مجله علوم پزشکی رازی, pp. 98-108, 1399.
47. ] 47[ B. Diao, K. Wen, J. Chen, Y. Liu, Z. Yuan, C. Han, J. Chen, Y. Pan, L. Chen, Y. Dan, J. Wang, Y. Chen, G. Deng, H. Zhou و Y. Wu, "Diagnosis of Acute Respiratory Syndrome Coronavirus 2 Infection by Detection of Nucleocapsid Protein," medRxiv, pp. 1-13, 2021.
48. ] 48[ G. Chowell, L. Sattenspiel, S. Bansal و C. Viboud, "Early sub-exponential epidemic growth: Simple models, nonlinear incidence rates, and additional mechanisms," Physics of Life Reviews, pp. 1-5, 2016.
49. ] 49[ B. Cantó, C. Coll و E. Sánchez, "Estimation of parameters in a structured SIR model," Advances in Difference Equations, pp. 1-13, 2017.
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