Smart Healthcare System Design. Группа авторов

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a rather real-life-oriented study [19], Pythagorean fuzzy data were considered, in which different evaluation data were provided in the form of Pythagorean fuzzy decision matrices regarding the feasible alternatives. The entries were taken from the views of experts and were described by fuzzy numbers from Pythagoras Table 2.2. In order to solve the resulting MCDM problems under uncertainty, they also broadened the application of the classical TOPSIS system. The most appropriate location and priority setting for buying the best healthcare technology could be decided by this process.

      In another fresh-taste study [20], the emphasis was on a muchdiscussed issue of workplace hazards, including protection and effectiveness of health workers against public abuse. To define and prioritize control measures of aggression, their innovative approach used fuzzy AHP and Fuzzy Additive Ratio Assessment. They described the solution as the best advice for controlling violence against health workers by increasing the number of security personnel and training staff.

      Below, Part C of Table 2.1 presents some very recent related articles published in highly acclaimed journals. This way, above deliberations, find ample scopes of research on applications of fuzzy set theory on the health-care and medicine problems.

      The IoT is a great blended domain for many fields such as mHealth application’s development. The mHealth application’s development is very trendy topic among the research community due to its direct involvement with the human’s life. These applications mostly focus on static patients but do not focus on the remote patient’s monitoring. The remote patient’s monitoring is getting fame due to fewer innovations and work is done in this domain. In this chapter we investigated different health issues. Additionally, the fuzzy logics work with a focus on their major components of the applications to develop for health monitoring is discussed. There is a strong need to address these all mentioned issues sot enhance the health sector both in eHealth and mHealth Environments.

      References

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      2. Ghorabaee, M.K., Developing an MCDM method for robot selection with interval type-2 fuzzy sets. Rob. Comput. Integr. Manuf., 1, 37, 221–232, 2016 Feb.

      3. Sen, D.K., Datta, S., Mahapatra, S.S., Extension of PROMETHEE for robot selection decision making. Benchmarking: An Int. J., 23, 4, 983–1014, 2016.

      4. Zhou, F., Wang, X., Goh, M., Fuzzy extended VIKOR-based mobile robot selection model for hospital pharmacy. Int. J. Adv. Rob. Syst., 15, 4, 1729881418787315, 2018 Dec.

      5. Kumar, P.M., Lokesh, S., Varatharajan, R., Babu, G.C., Parthasarathy, P., Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Gener. Comput. Syst., 1, 86, 527–534, 2018 Sep.

      6. Omrani, H., Shafaat, K., Emrouznejad, A., An integrated fuzzy clustering cooperative game data envelopment analysis model with application in hospital efficiency. Expert Syst. Appl., 30, 114, 615–628, 2018 Dec.

      7. Kumar, R., Pandey, A.K, Baz., A., Alhakami, H., Alhakami, W., Agrawal, A., Khan, R.A., Fuzzy-based symmetrical multi-criteria decision-making procedure for evaluating the impact of harmful factors of healthcare information security. Symmetry. 12, 4, 664, 2020 Apr.

      8. Tolga, C., Parlak, I.B., Castillo, O., Finite-interval-valued Type-2 Gaussian fuzzy numbers applied to fuzzy TODIM in a healthcare problem. Eng. Appl. Artif. Intell., Id. 103352. 2020.

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      12. Samuel, O.W., Asogbon, G.M., Sangaiah, A.K., Guanglin Li, F.P., An integrated decision support system based on ANN and Fuzzy AHP for heart failure risk prediction. Expert Syst. Appl., 68, 163–172, 2017.

      13. Mardani, A., Hooker, R., Ozkul, S., Yifan, S., Nilashi, M., Sabzi, H.Z., Fei, G., Application of decision making and fuzzy sets theory to evaluate the health-care and medical problems: A review of three decades of research with recent developments. Expert Syst. Appl., 137, 202–231, 2019.

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      15. Tucan, P., Gherman, B., Major, K., Vaida, C., Major, Z., Plitea, N., Carbone, G., Pisla, D., Fuzzy logic-based risk assessment of a parallel robot for elbow and wrist rehabilitation. Int. J. Environ. Res. Public Health, 17, 654, 2020.

      16. Tüzün, S. and Topcu, Y.I., A taxonomy of operations research studies in healthcare management. Oper. Res. Appl. HealthCare Manage., 3–21, 2017.

      17. Narayanamurthy, G., Gurumurthy, A., Is the hospital lean? A mathematical model for assessing the implementation of lean thinking in healthcare institutions. Oper. Res. HealthCare, 1, 18, 84–98, 2018 Sep.

      18. Suresh, M., Vaishnavi, V., Pai, R.D., Leanness evaluation in healthcare organizations using fuzzy logic approach. Int. J. Org. Anal., 2020.

      19. Akram, M., Dudek, W.A., Ilyas, F., Group decision-making based on pythagorean fuzzy TOPSIS method. Int. J. Intell. Syst., 1–21, 2019.

      20. Rajabi, F., Jahangiri, M., Bagherifard, F., Banaee, S., Farhadi, P., Strategies for controlling violence against healthcare workers: Application of fuzzy analytical hierarchy process and fuzzy additive ratio assessment. J. Nurs. Manage., 28, 4, 777–786, 2020 May.

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      25. AlZu’bi, S., Shehab, M., Al-Ayyoub, M., Jararweh, Y., Gupta, B., Parallel implementation for 3d medical volume fuzzy segmentation. Pattern Recognit.

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