Machine Learning Applied to Cervical Cancer Data

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Dhwaani Parikh 1 Vineet Menon 1,*

1. RMIT University, 124 La Trobe St, Melbourne VIC 3000

* Corresponding author.


Received: 11 Jul. 2018 / Revised: 12 Sep. 2018 / Accepted: 16 Oct. 2018 / Published: 8 Jan. 2019

Index Terms

Cancer, Cervical cancer, Decision tree Classifier, herpes virus, K-nearest neighbor, Machine learning, Random forest


Cervical Cancer is one of the main reason of deaths in countries having a low capita income. It becomes quite complicated while examining a patient on basis of the result obtained from various doctor’s preferred test for any automated system to determine if the patient is positive with the cancer. There were 898 new cases of cervical cancer diagnosed in Australia in 2014. The risk of a woman being diagnosed by age 85 is 1 in 167. We will try to use machine learning algorithms and determine if the patient has cancer based on numerous factors available in the dataset. Predicting the presence of cervical cancer can help the diagnosis process to start at an earlier stage.

Cite This Paper

Dhwaani Parikh, Vineet Menon,"Machine Learning Applied to Cervical Cancer Data", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.5, No.1, pp.53-64, 2019. DOI: 10.5815/ijmsc.2019.01.05


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