Breast Cancer Risk Analysis using Machine Learning


Dattatraya Adane
Laxmikant Kabra
Akansha Banode
Mansi Agrawal


When cells in and around breast are affected and damaged due to cancer, we call it Breast Cancer. It is commonly
found among women and a very few men. Breast Cancer poses major health hazard and best way to handle it
is to identify the symptoms as early as possible. Recently, Machine Learning techniques have been aggressively
used by many researchers for different types of analysis and predictions in medical domain. In literature, Breast
Cancer classification and prediction through Machine Learning techniques, based on Breast Cancer Wisconsin
Data Set, has come into picture many times. Typical attributes like radius, perimeter, tumour size (often fetch
through X-Rays) apart from others, provides comprehensive inputs for the prediction process, often at a later
stage. However, till date, work on evaluation of the Risk of Breast Cancer is quite limited. We have worked on
the problem of risk prediction of breast cancer on the basis of self-assessed parameters, to find if the patient is
likely to get the disease, at a very early stage. Risk evaluation / prediction tries to identify if a person is at risk
of getting infected with disease. Risk analysis can not only save money but also enable the patient to undertake
course correction in terms of food intake and medication, before it is too late. In this paper we present our analysis
based on well-defined parameters, discuss our results and then compare those results with one other similar work.


How to Cite
Adane, D., Kabra, L., Banode, A., & Agrawal, M. (2021). Breast Cancer Risk Analysis using Machine Learning. International Journal of Next-Generation Computing, 12(5).


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