Adaptive boosting model for breast cancer prediction

• Published in: Journal of intelligent & fuzzy systems 2023-01, Vol.45 (2), p.3417-3431
• Main Authors: Sherubha, P., Jubair Ahmed, L., Kannan, K.S., Sasirekha, S.P.
• Format: Article
• Language: English
• Subjects: Breast cancer; Error reduction; Gene expression; Health services; Internet of medical things; Machine learning; Medical prognosis; Modal data; Performance prediction; Prediction models; Prognosis
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-230086

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.