RETRACTED: A novel and powerful framework based on neutrosophic sets to aid patients with cancer (Retracted Article)

Developments in the internet of things technology (IoT) have greatly influenced the way we live and have been seen in a variety of application areas, including health care. Most of the papers reviewed dealt with some health care services, and there is little scientific literature on the application or implementation of IoT in cancer detection and treatment services. This encouraged us to (re) evaluate the provision of health-care services and their location to harness the benefits associated with the use of IoT. Also, the lateness which caused by transmitting data to the cloud and back to the application is inadmissible for healthcare systems which based on IoT. As well, it is not efficient to transmit so much data to the cloud for processing and storage, because it would saturate network bandwidth and not be scalable. This also encouraged us to use fog computing which take advantages of cloud resources and coordinate the utilization of geographically distributed edge devices. So, this paper proposes the implementation of healthcare system which based on IoT and fog computing technologies for averting the spread of cancer via early detection, evaluation of disease's symptoms, and also analyzing health data that gathered from IoT through various sensor networks and other smart devices to help healthcare professionals to turn a stream of data into actionable insights and evidence-based healthcare decision-making to improve and enhance cancer treatment. Due to the similarity of the general symptoms of cancer with some other diseases , we also proposed a neutrosophic multi-criteria decision making technique for assisting healthcare professionals to predict disease that patient may be suffers from if he/she does not have cancer. This technique depends on gathered data from proposed healthcare system and able to handle uncertainty of imprecision and ambiguity which resulted from the proportional priority scales of different symptoms of disease. (C) 2018 Elsevier B.V. All rights reserved.