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Prediction of Breast Cancer using Decision tree and Random Forest Algorithm

N.Sridevi 1 , S.Anitha 2

  1. Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.
  2. Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.

Correspondence should be addressed to: sridevi.n78@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-2 , Page no. 226-229, Feb-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i2.226229

Online published on Feb 28, 2018

Copyright © N.Sridevi, S.Anitha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: N.Sridevi, S.Anitha , “Prediction of Breast Cancer using Decision tree and Random Forest Algorithm,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.226-229, 2018.

MLA Style Citation: N.Sridevi, S.Anitha "Prediction of Breast Cancer using Decision tree and Random Forest Algorithm." International Journal of Computer Sciences and Engineering 6.2 (2018): 226-229.

APA Style Citation: N.Sridevi, S.Anitha , (2018). Prediction of Breast Cancer using Decision tree and Random Forest Algorithm. International Journal of Computer Sciences and Engineering, 6(2), 226-229.

BibTex Style Citation:
@article{_2018,
author = {N.Sridevi, S.Anitha },
title = {Prediction of Breast Cancer using Decision tree and Random Forest Algorithm},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2018},
volume = {6},
Issue = {2},
month = {2},
year = {2018},
issn = {2347-2693},
pages = {226-229},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1728},
doi = {https://doi.org/10.26438/ijcse/v6i2.226229}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i2.226229}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1728
TI - Prediction of Breast Cancer using Decision tree and Random Forest Algorithm
T2 - International Journal of Computer Sciences and Engineering
AU - N.Sridevi, S.Anitha
PY - 2018
DA - 2018/02/28
PB - IJCSE, Indore, INDIA
SP - 226-229
IS - 2
VL - 6
SN - 2347-2693
ER -

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Abstract

Breast cancer is one of the most leading causes of death among women. The early detection of anomalies in breast enables the doctor’s in diagnosing the breast cancer easily which can save numerous of lives. In this work, Wisconsin Diagnosis Breast Cancer database is used for experiments in order to predict the breast cancer either benign or malignant. Supervised Machine Learning algorithms namely Decision tree and Random Forests are used to classify the breast cancer. R programming language is used to classify the breast cancer. The performances of the algorithms are measured in terms of accuracy, specificity and sensitivity. The functionality of the algorithms are analysed and the results were discussed.

Key-Words / Index Term

Breast Cancer, Classification, Decision tree, Random Forests, R programming

References

[1] Jain R, “Introduction to data mining techniques”, hhtp:// www.iasri.res.in/ebook/expertsystem/datamining.pdf
[2] Borges and Lucas Rodrigues, “Analysis of Wisconsin Breast Cancer Dataset and Machine Learning for Breast Cancer Detection”, Proceedings of XI Workshop de Visão Computational, October 05th‐07th, 2015.
[3] Dubey, A.K., Gupta, U. & Jain, S, “Analysis of k-means clustering approach on the breast cancer Wisconsin dataset”, International Journal of Computer Assisted Radiology and Surgery, Vol.11, Issue 11, pp. 2033–2047, November 2016 .
[4] P.Dhivyapriya and Dr.S.Sivakumar, “Classification of Cancer Dataset in Data Mining Algorithms Using R Tool”, International Journal of Computer Science Trends and Technology (IJCST) – Vol.5, Issue 1, Jan – Feb 2017
[5] F.Paulin et al., “Classification of Breast cancer by comparing Back propagation training algorithms”, International Journal of Computer Sciences and Engineering (IJCSE), Vol 3,No 1, pp 327 – 332,Jan 2011.