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A New Paradigm for Healthcare System Using Emerging Technologies

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Applied Computational Technologies (ICCET 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 303))

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Abstract

There are many diseases threatening humans around the globe. Many of them are from the past centuries, and a few are newly discovered. This study has mainly focused on trending technologies such as Artificial Intelligence, Machine Learning, Big Data, Internet of Things that are used to predict diseases in the health sector. This study has collected data from the previously published articles from the reputed publishers using a systematic review approach, and these data were analyzed separately for each technology mentioned above. Studies confirmed that most of the research focused on IoT in the health sector. Furthermore, all the above technologies provide higher accuracy in predicting diseases. But, IoT provides higher accuracy than other emerging technologies for predicting most diseases. A few constraints of the study were the size of the dataset and missing quality qualities. In the end, it is recommended to study the security issues in IoT in the healthcare sector to predict and diagnose diseases.

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References

  1. Ali, M.Z., Hossain, S., Muhammad, G., Sangaiah, A.K.: An intelligent healthcare system for detection and classification to discriminate vocal fold disorders. Fut. Gener. Comput. Syst. 85, 19–28 (2018). https://doi.org/10.1016/j.future.2018.02.021

    Article  Google Scholar 

  2. Topol, E.: The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. Basic Books (2012)

    Google Scholar 

  3. Laplante, P.A., Laplante, N.L.: A structured approach for describing healthcare applications for the Internet of Things. In: Proceedings of the IEEE 2nd World Forum Internet Things (WF-IoT), pp. 621–625 (2015)

    Google Scholar 

  4. Pino, C., Di Salvo, R.: A survey of cloud computing architecture and applications in health. In: International Conference on Computer Science and Electronics Engineering, pp. 1649–1653 (2013)

    Google Scholar 

  5. Aldahiri, A., Alrashed, B., Hussain, W.: Trends in using IoT with machine learning in health prediction system. Forecasting 3(1), 181–206 (2021)

    Article  Google Scholar 

  6. Zeadally, S., Siddiqui, F., Baig, Z., Ibrahim, A.: Smart healthcare: challenges and potential solutions using Internet of things (IoT) and big data analytics. PSU Res. Rev. 4(2), 149–168 (2019)

    Article  Google Scholar 

  7. Tekkesin, A.I.: Artificial intelligence in healthcare: past, present and future. Anatol. J. Cardiol. 22, 8–9 (2019)

    Google Scholar 

  8. Wu, T., Redouté, J.M., Yuce, M.R.: A wireless implantable sensor design with subcutaneous energy harvesting for long-term IoT healthcare applications. IEEE Access. 6, 35801–35808 (2018). https://doi.org/10.1109/ACCESS.2018.2851940

    Article  Google Scholar 

  9. Tsikala Vafea, M., et al.: Emerging technologies for use in the study, diagnosis, and treatment of patients with COVID-19. Cell. Mol. Bioeng. 13(4), 249–257 (2020). https://doi.org/10.1007/s12195-020-00629-w

    Article  Google Scholar 

  10. Khan, Z.F., Alotaibi, S.R.: Applications of artificial intelligence and big data analytics in m-health: a healthcare system perspective. J. Healthc. Eng. 2020 (2020)https://doi.org/10.1155/2020/8894694

  11. Chen, M., Hao, Y., Hwang, K., Wang, L., Wang, L.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5, 8869–8879 (2017). https://doi.org/10.1109/ACCESS.2017.2694446

    Article  Google Scholar 

  12. Jagadeeswari, V., Subramaniyaswamy, V., Logesh, R., Vijayakumar, V.: A study on medical Internet of Things and Big Data in personalized healthcare system. Health Inf. Sci. Syst. 6(1), 1–20 (2018). https://doi.org/10.1007/s13755-018-0049-x

    Article  Google Scholar 

  13. Qadri, Y.A., Nauman, A., Zikria, Y.B., Vasilakos, A.V., Kim, S.W.: The future of healthcare internet of things: a survey of emerging technologies. IEEE. Commun. Surv. Tut. 22, 1121–1167 (2020). https://doi.org/10.1109/COMST.2020.2973314

    Article  Google Scholar 

  14. Dineshkumar, P., Senthilkumar, R., Sujatha, K., Ponmagal, R.S., Rajavarman, V.N.: Big data analytics of IoT based Health care monitoring system. In: 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering, UPCON 2016, pp. 55–60 (2017). https://doi.org/10.1109/UPCON.2016.7894624

  15. Balakrishna, S., Thirumaran, M., Solanki, V.K.: IoT sensor data integration in healthcare using semantics and machine learning approaches. In: Balas, V.E., Solanki, V.K., Kumar, R., Ahad, M.A.R. (eds.) A Handbook of Internet of Things in Biomedical and Cyber Physical System. ISRL, vol. 165, pp. 275–300. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-23983-1_11

    Chapter  Google Scholar 

  16. Wan, J., et al.: Wearable IoT enabled real-time health monitoring system. EURASIP J. Wirel. Commun. Netw. 2018(1), 1 (2018). https://doi.org/10.1186/s13638-018-1308-x

    Article  Google Scholar 

  17. Tunc, M.A., Gures, E., Shayea, I.: A Survey on IoT Smart Healthcare: Emerging Technologies, Applications, Challenges, and Future Trends (2021)

    Google Scholar 

  18. Yeole, A.S., Kalbande, D.R.: Use of Internet of Things (IoT) in healthcare: a survey. In: ACM International Conference Proceeding Series, 21–22-March, pp. 71–76 (2016). https://doi.org/10.1145/2909067.2909079

  19. Mahmud, R., Koch, F.L., Buyya, R.: Cloud-fog interoperability in IoT-enabled healthcare solutions. In: ACM International Conference Proceeding Series (2018). https://doi.org/10.1145/3154273.3154347

  20. Elhoseny, M., Ramírez-González, G., Abu-Elnasr, O.M., Shawkat, S.A., Arunkumar, N., Farouk, A.: Secure medical data transmission model for IoT-based healthcare systems. IEEE Access 6, 20596–20608 (2018). https://doi.org/10.1109/ACCESS.2018.2817615

    Article  Google Scholar 

  21. Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., Vento, M.: Trends in IoT based solutions for health care: moving AI to the edge. Pattern Recogn. Lett. 135, 346–353 (2020). https://doi.org/10.1016/j.patrec.2020.05.016

    Article  Google Scholar 

  22. Sobhan Babu, B., Srikanth, K., Ramanjaneyulu, T., Lakshmi Narayana, I.: IoT for Healthcare (2013)

    Google Scholar 

  23. Azzawi, M.A., Hassan, R., Azmi, K., Bakar, A.: A Review on Internet of Things (IoT) in Healthcare Academic Entrepreneurship View project Internet of Things View project (2016)

    Google Scholar 

  24. Institute of Electrical and Electronics Engineers. Delhi Section, Institute of Electrical and Electronics Engineers: 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT): Proceedings: 11 March–13 March 2016, New Delhi, India, pp. 237–242. IEEE (2016)

    Google Scholar 

  25. Arulanthu, P., Perumal, E.: An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction. Int. J. Imaging Syst. Technol. 30, 815–827 (2020). https://doi.org/10.1002/ima.22424

    Article  Google Scholar 

  26. Fouad, H., Hassanein, A.S., Soliman, A.M., Al-Feel, H.: Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction. Meas. J. Int. Meas. Confed. 159, 107757 (2020). https://doi.org/10.1016/j.measurement.2020.107757

    Article  Google Scholar 

  27. Bharathi, R., et al.: Energy efficient clustering with disease diagnosis model for IoT based sustainable healthcare systems. Sustain. Comput. Inf. Syst. 28, 100453 (2020). https://doi.org/10.1016/j.suscom.2020.100453

    Article  Google Scholar 

  28. Kashani, M.H., Madanipour, M., Nikravan, M., Asghari, P., Mahdipour, E.: A systematic review of IoT in healthcare: applications, techniques, and trends. J. Netw. Comput. Appl. 192, 103164 (2021). https://doi.org/10.1016/j.jnca.2021.103164

    Article  Google Scholar 

  29. Muthu, B., et al.: IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector. Peer-to-Peer Netw. Appl. 13(6), 2123–2134 (2020). https://doi.org/10.1007/s12083-019-00823-2

    Article  Google Scholar 

  30. Herrera Perez, J.L., Fajes Alfonso, A., Alvarez, D.: Retinopatia Diabetica E Hiperlipoproteinemia. Rev. Cubana Med. 28, 333–340 (1989)

    Google Scholar 

  31. Wu, T., Wu, F., Redoute, J.M., Yuce, M.R.: An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access 5, 11413–11422 (2017). https://doi.org/10.1109/ACCESS.2017.2716344

    Article  Google Scholar 

  32. Yeh, K.H.: A secure IoT-based healthcare system with body sensor networks. IEEE Access. 4, 10288–10299 (2016). https://doi.org/10.1109/ACCESS.2016.2638038

    Article  Google Scholar 

  33. Pike, M., Mustafa, N.M., Towey, D., Brusic, V.: Sensor networks and data management in healthcare: emerging technologies and new challenges. In: Proceedings of the International Computer Software and Application Conference, vol. 1, pp. 834–839 (2019). https://doi.org/10.1109/COMPSAC.2019.00123

  34. Schwalbe, N., Wahl, B.: Artificial intelligence and the future of global health. Lancet 395, 1579–1586 (2020). https://doi.org/10.1016/S0140-6736(20)30226-9

    Article  Google Scholar 

  35. Reddy, U.S., Thota, A.V., Dharun, A.: Machine learning techniques for stress prediction in working employees. In: 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), vol. 2018, pp. 1–4 (2018). https://doi.org/10.1109/ICCIC.2018.8782395

  36. Winter, G.: Machine learning in healthcare. Br. J. Healthc. Manage. 25(2), 100–101 (2019). https://doi.org/10.12968/bjhc.2019.25.2.100

    Article  Google Scholar 

  37. Sarwar, M.A., Kamal, N., Hamid, W., Shah, M.A.: Prediction of diabetes using machine learning algorithms in healthcare. In: 2018 24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing, ICAC 2018, pp. 1–6 (2018). https://doi.org/10.23919/IConAC.2018.8748992

  38. Abdelaziz, A., Elhoseny, M., Salama, A.S., Riad, A.M.: A machine learning model for improving healthcare services on cloud computing environment. Meas. J. Int. Meas. Confed. 119, 117–128 (2018). https://doi.org/10.1016/j.measurement.2018.01.022

    Article  Google Scholar 

  39. Liao, W., Zhang, A., Shih, S.: Machine learning methods applied to predict ventilator-associated pneumonia with pseudomonas aeruginosa infection via sensor array of electronic nose in intensive care unit. Sensors 19(8), 1866 (2019). https://doi.org/10.3390/s19081866

    Article  Google Scholar 

  40. Alshamrani, M.: IoT and artificial intelligence implementations for remote healthcare monitoring systems: a survey. J. King Saud Univ. Comput. Inf. Sci. (2021). https://doi.org/10.1016/j.jksuci.2021.06.005

  41. Abdali-Mohammadi, F., Meqdad, M.N., Kadry, S.: Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms. IAES Int. J. Artif. Intell. (IJAI) 9(4), 766 (2020). https://doi.org/10.11591/ijai.v9.i4.pp766-771

    Article  Google Scholar 

  42. Carnaz, G., Nogueira, V.: An Overview of IoT and Healthcare (2016)

    Google Scholar 

  43. Kaur, P., Sharma, M., Mittal, M.: Big Data and machine learning based secure healthcare framework. Procedia Comput. Sci. 132, 1049–1059 (2018). https://doi.org/10.1016/j.procs.2018.05.020

    Article  Google Scholar 

  44. Agarwal, R., Dugas, M., Gao, G.(Gordon), Kannan, P.K.: Emerging technologies and analytics for a new era of value-centered marketing in healthcare. J. Acad. Mark. Sci. 48, 9–23 (2020). https://doi.org/10.1007/s11747-019-00692-4

  45. Khan, W.Z., Rehman, M.H., Zangoti, H.M., Afzal, M.K., Armi, N., Salah, K.: Industrial Internet of things: recent advances, enabling technologies and open challenges. Comput. Electr. Eng. 81, 106522 (2020). https://doi.org/10.1016/j.compeleceng.2019.106522

    Article  Google Scholar 

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Correspondence to C. M. M. Mansoor .

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Mansoor, C.M.M., Nafrees, A.C.M., Aysha Asra, S., Jahan, M.U.I. (2022). A New Paradigm for Healthcare System Using Emerging Technologies. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_29

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