Breast Cancer Diagnosis Using Data Mining Classification Techniques Using Weka

Authors

  • Vandana Bharadi
  • Nidhi Mishra

DOI:

https://doi.org/10.37591/rtpl.v4i3.1351

Abstract

Breast cancer is currently posing a serious threat and is the second leading cause of death in women. A good and accurate diagnosis is important in order to control the very high recurrence rate of breast cancer. In this work, we explore the applicability of various data mining classifier to predict the presence of breast cancer. The performance of conventional supervised learning algorithms such as NaïveBayes, J4.8, SMO, IBk and MLP has been analyzed in this paper. Experipmental results are hereby proving that SMO to be the best one with highest accuracy and specificity, an average minimal error rate.

Keywords: Data mining, data classification, breast cancer diagnosis, Naïve Bayes, J4.8,SMO, MLP, IBk

Cite this Article
Vandana Bharadi, Nidhi Mishra. Breast Cancer Diagnosis Using Data Mining Classification Techniques Using WEKA. Recent Trends in Programming Languages. 2017; 4(3): 7–15p.


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Published

2018-03-21

Issue

Vol. 4 No. 3 (2017)

Section

Research Articles

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