Abstract
Now a day Breast cancer (BC) is very common and terrific disease in women, most detected and second leading cause of the ladies’ demise from the worldwide. Big number of people is passing their life or poor survival rate is because of this disease every year. Females are at high risk of BC, so it became quite essential and necessary for doctors to choose for an exact and suitable treatment for avoidance and remedy of cancer patients. So the basic motive is to find the cancer cells very correctly. Forecasting and categorization of BC using an effective and correct model of machine learning (ML) is essential for creation a new type of BC prognostic and diagnostic policies that really give a reduction push to the sufferer. Diversified technology, including Bayesian classifiers, Artificial Neural Networks and Decision Trees have been commonly applied in cancerous tumor. Undoubtedly methods used for Machine Learning may increase our understanding about breast cancer prediction and progression. It is important to consider these approaches in daily clinical practice. Neural networks are now a day’s very key and popular field in computational biology, chiefly in the area of radiology, oncology, cardiology and urology. In this study, we had summarized numerous ML techniques which could be used as an important tool by surgeons for timely detection, and prediction of cancerous cells has been studied and introduced.
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Maurya, R.K., Yadav, S.K., Tewari, P. (2020). Epidemiology of Breast Cancer (BC) and Its Early Identification via Evolving Machine Learning Classification Tools (MLCT)–A Study. In: Dehuri, S., Mishra, B., Mallick, P., Cho, SB., Favorskaya, M. (eds) Biologically Inspired Techniques in Many-Criteria Decision Making. BITMDM 2019. Learning and Analytics in Intelligent Systems, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-39033-4_11
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DOI: https://doi.org/10.1007/978-3-030-39033-4_11
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