Breast Cancer Prediction Using Machine Learning

Breast Cancer Prediction Using Machine Learning

Authors

  • Ankitha Philip
  • Detty Stanly

DOI:

https://doi.org/10.5281/zenodo.6228080

Keywords:

machine learning, support vector machine, correlation, KNN algorithm, 10-fold cross validation, confusion matrix

Abstract

Breast Cancer is considered to be a major threat to
women which leads to an increase in death rates of women that
causes a major concern in the community. Even though this disease
is a major threat but today medical-science is capable enough to
rehabilitate such threats without causing any harm to women if
detected at early stages using their innovative thoughts. Dredging
the cancer and transforming between the diagnosis that certify
whether the patient has breast cancer or not is considered to be the
major provocation. Women worldwide are distressed by breast
cancer, a widely affecting health issue that can use a large number
of deaths. This paper aims to review and present an approach to
identify the accuracy of breast cancer using Machine Learning. The
objective is to investigate the application of multiple algorithms
based on Machine Learning approach for early breast cancer
detection. Machine learning algorithms are needed to identify
cancers based on a given set of data, that is why automation is
needed. It aims to make computers capable of self-learning. Other
than relying on pre-programmed models, it is based on identifying
patterns in observed data to predict outcomes.

Author Biography

Ankitha Philip

 

 

Published

2023-02-23

How to Cite

Ankitha Philip, & Detty Stanly. (2023). Breast Cancer Prediction Using Machine Learning. National Conference on Emerging Computer Applications, 4(1). https://doi.org/10.5281/zenodo.6228080
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