@article{MAKHILLAJIT202019116815,
    title = {Predictive Model for Likelihood of Survival among Breast Cancer Patients using Machine
Learning Techniques},
    journal = {Asian Journal of Information Technology},
    volume = {19},
    number = {11},
    pages = {263-269},
    year = {2020},
    issn = {1682-3915},
    doi = {ajit.2020.263.269},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2020.263.269},
    author = {Ajinaja and},
    keywords = {Breast cancer,survival,Nigeria,predictive model,Naive Baye,machine learning},
    abstract = {Providing a prediction model that can give
survival rate of breast cancer patients among women
based on past records collected over the years in an
underdeveloped country like Nigeria poses a challenge.
This is because of their poor data collection habit and
underdeveloped health care system. Machine Learning
(ML) offers a different approach and cheaper alternative
of identifying survival rate among breast cancer patients
among women. The purpose of this study is to provide
survival rate or mortality rate of breast cancer patients
after treatments has been administered. Naive Baye’s
Machine learning techniques was used in developing a
predictive model to predict survival rate of breast cancer
patients among women. Data was gathered from 30
different health centre location ranging from hospitals and
institute. The data included all women who have been
diagnosed with breast cancer from 2000-2005 and all
death cases encountered so far. The simulation of the
model was done using R Studio software. The result of
the model was good as survival rate was above 85%
showing incredible in the model used. Comparisons were
made between some of the factors affecting breast cancer
and survival rate using box plot. The results showed there
is high survival rate in breast cancer patients among
women in Nigeria. Other ML techniques can also be
considered using same data to further improve the model.}
    }