Design of NBW-MHO with BERT model for prediction of Breast Cancer in IoT Healthcare System

Abstract

The key to successful early recovery and treatment of breast cancer in today's healthcare system is an accurate and prompt diagnosis. Over the last several years, the IoT has undergone a transition that makes it possible to analyse both real-time techniques. Medical diagnostics are aided by the Internet of Medical Things, which connects various medical equipment and artificial intelligence applications with the healthcare network. Most women with breast cancer don't make it because the disease isn't detected early enough using today's best methods. Therefore, doctors and scientists are confronted with a significant challenge in recognizing breast cancer at an primary stage. We present a medical IoT-based diagnostic system that can distinguish between patients with cancer and those without it in an Internet of Things setting. Malignant vs benign categorization is performed using an unique transfer learning technique called BERT, which is based on a previously learned language model. In particular, this research looks at how well novel fine-tuning approaches based on transfer learning might improve BERT's capacity to capture significant context. This research improves the BERT model's classification accuracy by using a Black Widow-meta-heuristic Optimization (NBW-MHO) feature selection strategy to refine feature selection from the breast cancer dataset. The WDBC dataset served as a testbed for the suggested method. The suggested model's classification accuracy using the BERT model and NBW-MHO was 95.20 percent.