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  • br As appears in Fig the


    As appears in Fig. 6, the specialists and consultants decided that this patient suffers from AIDS disease.
    6. Conclusions and future works
    For any country and healthcare agencies, the cancer disease is a global challenge. In this paper, we suggested IoT-based fog computing model for detecting and monitoring cancer. The social interactions and symptoms of user’s body are captured via using
    Fig. 5. Hierarchical structure of problem.
    Fig. 6. Evaluation results of diseases.
    WBAN and mobile application interface. After collecting personal information and symptoms, we classified users into infected or uninfected persons. If user is infected person, then the type, stage, and treatment method of cancer determines through the proposed system. But if user is uninfected person (does not have cancer) and suffers from symptoms which are similar to cancer’s symptoms, we proposed a neutrosophic MCDM technique to extra examine and forecast certain differences of diseases based on reported symptoms of patient. The proposed technique has achieved many advantages for transacting with uncertain and inconsistent infor-mation which exist in MCDM problems.
    From time to time, the patients are not ready to carry sensors on their bodies, so our future trend is to design a smart heath care system which support this part and achieve users satisfaction.
    Limitation of Proposed Study: More posts from more firms will make our study better.
    Competing Interests: The authors announce that there is no dis-crepancy of interests concerning the publication of this research.
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    Mohamed Abdel-Basset Received his B.Sc., M.Sc and the Ph.D in Information systems and technology from Faculty of Computers and Informatics, Zagazig University, Egypt. His current research interests are Optimization, Opera-tions Research, Data Mining, Computational Intelligence, Applied Statistics, Decision support systems, Robust Opti-mization, Engineering Optimization, Multi-objective Op-timization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is working on the application of multi-objective and robust meta-heuristic optimization techniques. He is also an/a Editor/reviewer