A distributed key authentication and OKM-ANFIS scheme based breast cancer prediction system in the IoT environment

The Internet of Things (IoT) has significantly upgraded in medical and health care. This technology aids the patients as well as doctors for envisaging an assortment of diseases precisely and diagnoses these diseases as per the outcomes. However, the prevailing research methodologies encompass the issue of poor diagnostic accuracy in addition to safe data transfer betwixt IoT and cloud storage. This paper proposed a distributed key authentication in addition to OKM-ANFIS cantered breast cancer (BC) prediction system on the IoT environment to trounce such disadvantages also, the research used GA for the prediction of multi models. Initially, the authentication is performed by means of the patient. Then, the sensed values are attained as of the ' sensors that are placed inside the bra. Later, the DK-AES algorithm uploads the attained data safely to the hospital public cloud server (CS). Subsequently, the hospital management (HM) system downloads the data securely. The HM-system envisages BC in '2' phases: (1) pre-processing and (2) prediction. Utilizing removal redundancy, replacement of missing attributes, along with normalization, the data is pre-processed. Subsequently, the OKM-ANFIS classification algorithm predicts the disease. If any critical concerns arise, an alert text is sent by the HM to the patient's mobile. In an experimental assessment, the proposed work renders better outcomes than the prevailing methods.

Automated summary

The study utilizes IoT technology and algorithms to predict breast cancer, securely uploading data to cloud server through distributed key authentication, and then predicting the disease through preprocessing and classification algorithms, sending alert messages to patients when necessary.