No abstract

Breast cancer occurs when cells in the breast grow out of control. Breast cancer canspread outside the breast through lymph vessels and blood vessels when it spreads to other parts of thebody, it is said to have metastasized. Most breast cancer cases are reported in women who are 50 yearsand/or o40 years older. According to facts and figures shared by WHO (World Health Organization), itimpacts 2.1 million women every year and also causes the greatest number of cancer-related deathsamongst women. Whilst breast cancer rates are higher among women in more developed regions, ratesare increasing in nearly every region globally. Different machine learning algorithms have beenapplied to the dataset like Naïve Bayes (NB), J48 Decision tree, K-Nearest Neighbor (KNN) and ANN(Gradient Descent) have been applied among them ANN (Gradient Descent) produces the optimalresults among these classification algorithms. The proposed Internet of Medical Things EnabledCloud-Based Breast Cancer Identification with Machine Learning system model with 98.07 %accuracy has been achieved. For the proposed model 97.64 % sensitivity and 98.32 % specificity havebeen recorded. From the results produced by the proposed expert system, it's satisfactory to utilize itfor breast cancer diagnosis. The Proposed system model will be helpful for the diagnosis of breastcancer.

The Internet of Medical Things (IoMT) is the networking of health things or equipment that communicate data over the internet without the need for human involvement in the healthcare field. A large quantity of data is collected from numerous sensors in the health field, and it is all transferred and stored on the cloud. This data is growing bigger here all time, and it's becoming increasingly challenging to secure it on the cloud with real-time storage and computing. Data security problem can be addressed with the aid of machine algorithms and fog computing. For data security in IoMT gadgets correspondence in an intelligent fashion, an intelligent encryption algorithm (IEA) is proposed using blockchain technology in cloud based system framework (CBSF). It is applied on patient’s database to provide immutable security, tampering prevention and transaction transparency at the fog layer in IoMT.  The suggested expert system's results indicate that it is suitable for use in for the security. In the fog model, the blockchain technology approach also helps to address latency, centralization, and scalability difficulties.