Prediction of Breast Cancer using Decision tree and Random Forest Algorithm
DOI:
https://doi.org/10.26438/ijcse/v6i2.226229Keywords:
Breast Cancer, Classification, Decision tree, Random Forests, R programmingAbstract
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.
References
Jain R, “Introduction to data mining techniques”, hhtp:// www.iasri.res.in/ebook/expertsystem/datamining.pdf
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.
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 .
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
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.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
