Affiliations: [a] Department of Information Technology, Karpagam College of Engineering, Coimbatore, India
| [b] Department of Electronics and Communication Engineering, Akshaya College of Engineering and Technology, Coimbatore, India
| [c] Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education (Deemed to University), Krishnankovil, Virudhunagar, Tamilnadu, India
| [d] Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India
Correspondence:
[*]
Corresponding author. P. Sherubha, Department of Information Technology, Karpagam College of Engineering, Coimbatore, India. E-mail: [email protected].
Abstract: The aggressive form of cancer commonly in breast cells is breast cancer. The highly aggressive form of cancer is frequently created in breast cells. The need for the predictive model to accurately measure the prognosis prediction of breast cancer in the earlier stage is highly recommended. This development of methods for protecting people from fatal diseases by the researchers from the different disciplines who are all working altogether. An accurate breast cancer prognosis prediction is made by using a good predictive model to assist Medical Internet of Things (mIoT). Various advantages such as cancer detection in an earlier stage, medical expenses related to treatment, and having unwanted treatment gives the accurate prediction attains spare patients. Existing models lie on the uni-modal data such as chosen gene expression to predict the model’s design. Few learning-based predictive models are used in the proposed method to improve breast cancer prognosis prediction from the current data sets. Most of the peculiar benefits of the suggested method rely on the model’s architecture. Here, a novel adaptive boosting model (a-BM) is used to measure the loss function of every individual and intends to reduce the error rate. Various performances metrics are used to evaluate the predictive performance, which provides the model gives a good outcome rather than the previous techniques.
Keywords: Machine learning, breast cancer, prediction rate, loss function, error rate
