Breast Cancer Predication Using Machine Learning and Data Mining

Authors

  • Swetha K  Sri Krishna College of Technology, Kovaipudur, Tamil Nadu, India
  • Ranjana R  Sri Krishna College of Technology, Kovaipudur, Tamil Nadu, India

Keywords:

WEKA, IBK, Simple Logistic, Naive Bayes, Decision Table, Multilayer

Abstract

Breast cancer is a type of cancer that emerge in breast of a women or a men. mostly women are affected by breast cancer than men. it is important to know that most breast lumps are benign and not a cancer. There are different types of breast cancer and most common type of breast cancer includes ductal carcinoma in situ (DCIS) and invasive carcinoma. classification and data mining methods are an effective way to classify data. Most commonly in medical field, where those are widely used in diagnosis of breast cancer. Women with 40 to 50 or older are average risk of breast cancer. women nearly age of 30 are mostly affected by the risk of breast cancer. Closely in 2012 1. 7 million new breast cancer cases were diagnosed. Breast cancer is mosttly diagnosed among women for breast cancer for 140 of 180 countries. After skin cancer Breast cancer is the most common cancer among American women. Nearly 500 men will die in breast cancer. 62 percent of breast cancer cases are diagnosed at a sectarian stage, for which 5 years of survival rate is 99%. Machine learning technique is used for prediction of breast cancer using data set. we have used WEKA tool for predicting the accuracy rate using various algorithms. Here we have used algorithms of IBK, Simple logistic, Naive bayes, Decision table, Multilayer perception.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-06-30

Issue

Volume 6, Issue 3, May-June-2020

Section

Research Articles

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How to Cite

[1]
Swetha K, Ranjana R, " Breast Cancer Predication Using Machine Learning and Data Mining" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.610-615, May-June-2020.