Dhwaani Parikh; Vineet Menon

Machine Learning Applied to Cervical Cancer Data

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Author(s)

Dhwaani Parikh ¹ Vineet Menon ^1,*

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

* Corresponding author.

DOI: https://doi.org/10.5815/ijmsc.2019.01.05

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

Abstract

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

Reference

[1]R), I & R), I 2018, "Improve Your Model Perfor- mance using Cross Validation (in Python / R)", An- alytics Vidhya, viewed 14 May, 2018, https://www.analyticsvidhya.com/blog/2018

[2]Anon 2018, "Accuracy, Precision, Recall & F1 Score: Interpretation of Performance Measures - Exsilio Blog", Exsilio Blog

[3]Anon 2018, S3.amazonaws.com, viewed 26May,2018, https://s3.amazonaws.com/MLMastery/MachineLearningAlgo-rithms.png? =8cjo2uzaybyqe5a1fipt

[4]Anon 2018, " Performance Measures - Exsilio Blog", Rmt Blog, viewed 26 May, 2018,<https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234>.

[5]Anon 2018, " Google ML", Blog, viewed 26 May, 2018,< https://www.guru99.com/r-generalized-linear-model.html>.

[6]Mishra, A. (2018, February 24). Metrics to Evaluate your Machine Learning Algorithm. Retrieved from https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234

[7]Narkhede, Sarang. “Understanding Confusion Matrix – Towards Data Science.” Towards Data Science, Towards Data Science, 9 Feb. 2018, towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62.

[8]Australia, Cancer Council. “Cervical Cancer.” CancerCouncilAustralia, www.cancer.org.au/about-cancer/types-of-cancer/cervical-cancer.html.

[9]Benjamin, S.M., Humberto, V.H., Barry, C.A. Use of Dagum Distribution for Modeling Tropospheric Ozone levels. J. Environ. Stat. 2013, 5(5).

[10]Dagum, C. A New Model of Personal Income Distribution: Specification and Estimation. Economie Appliqu´ee ; 1977, 30,413–437.

[11]Domma, F., Giordano S., Zenga, M. The Fisher information matrix on a type II doubly censored sample from a Dagum distribution. Appl. Mathe. Sci.2013,7, 3715–3729.

[12]Feigl, P. and Zelen, M. Estimation of exponential probabilities with concomitant information. Biometrics. 1965, 21, 826–838.

[13]Proschan, F. Theoretical explanation of observed decreasing failure rate. Technometrics.1963, 5, 375– 383.

[14]Naqash, S., Ahmad, S.P., Ahmed, A. Bayesian Analysis of Dagum Distribution. JRSS.2017, 10 (1), 123- 136.

[15]Kleiber, C., Kotz, S. Statistical Size Distributions in Economics and Actuarial Sciences. New York: Wiley. Meth and Appl. 2003, 18, 205–220.

International Journal of Mathematical Sciences and Computing (IJMSC)