Abstract
Computational intelligence (CI) and artificial intelligence (AI) have incredible roles to play in the development of smart and sustainable healthcare systems by facilitating the integration of smart technologies with conventional medical procedures. The Internet of Things (IoT) and CI healthcare systems rely heavily on data collection and machine learning since miniature devices represent the foundation and paradigm shift to sustainable healthcare. With these advancements in AI techniques, we can reduce our environmental impact, while simultaneously enhancing the quality of our services. Widespread use of these devices for innovative IoT applications, however, generates massive amounts of data, which can significantly strain processing power. There is still a need for an efficient and sustainable model in the area of disease predictions, such as lung cancer, blood cancer, and breast cancer. The fundamental purpose of this research is to prove the efficacy of a secure Internet of Medical Things (IoMT) in the detection and management of breast cancer via the use of gated recurrent units (GRUs), which are a more recent version of recurrent neural networks (RNNs). The blockchain has been employed to achieve the secure IoMT. Unlike long short-term memory units, they do not have a cell state of their own. Therefore, we have combined GRU with RNN to achieve the best results. When training a GRU-RNN classifier, it is typically necessary to collect tagged IoT data from many sources, which raises significant concerns about the confidentiality of the data. To verify the model, the experiment is performed on Wisconsin Diagnostic Breast Cancer (WDBC). The experimental result shows that the GRU-RNN has been archived 95% in terms of the accuracy metric, and the efficacy of the proposed IoMT model is superior to the existing approach in terms of accuracy, precision, and recall.