Abstract
Breast cancer is a vital cancer disease among women. The death rate increases predominantly due to the breast cancer tumor in such a way that one out of ten ladies are detected having breast malignancy. It is also second most cause for death of women in the USA. Thus, it is an important public health problem. Breast cancer is mostly categorized into two parts: benign and malignant. The early detection or prediction of this cancerous cell helps in preventing from higher death rates. In this paper, our main focus is to discuss and analyse different prediction models. The objective of the work is to design a classification model to predict the cancerous cells. In addition, a comparative analysis is to be performed among the classification techniques to yield an accurate classification result in the aim to finding out the classifier that works best in predicting the class with least error.
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Nanda, A., Manju, Gupta, S. (2022). Breast Cancer Prediction Models: A Comparative Study and Analysis. In: Poonia, R.C., Singh, V., Singh Jat, D., Diván, M.J., Khan, M.S. (eds) Proceedings of Third International Conference on Sustainable Computing. Advances in Intelligent Systems and Computing, vol 1404. Springer, Singapore. https://doi.org/10.1007/978-981-16-4538-9_41
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DOI: https://doi.org/10.1007/978-981-16-4538-9_41
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