BREAST CANCER RISK PREDICTION USING DATA MINING CLASSIFICATION TECHNIQUES

  • Kehinde Williams Department of Physical and Computer Sciences, College of Natural and Applied Sciences, McPherson University, Ajebo, Ogun State, Nigeria
  • Peter Adebayo Idowu Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
  • Jeremiah Ademola Balogun Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
  • Adeniran Ishola Oluwaranti Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
Keywords: breast cancer, classification, prediction, risk factors, naïve bayes, J48 decision trees

Abstract

Breast cancer poses serious threat to the lives of people and it is the second leading cause of death in women today and the most common cancer in women in developing countries in Nigeria where there are no services in place to aid the early detection of breast cancer in Nigerian women.  A number of studies have been undertaken in order to understand the prediction of breast cancer risks using data mining techniques.  Hence, this study is focused at using two data mining techniques to predict breast cancer risks in Nigerian patients using the naïve bayes’ and the J48 decision trees algorithms.  The performance of both classification techniques was evaluated in order to determine the most efficient and effective model.  The J48 decision trees showed a higher accuracy with lower error rates compared to that of the naïve bayes’ method while the evaluation criteria proved the J48 decision trees to be a more effective and efficient classification techniques for the prediction of breast cancer risks among patients of the study location.

Author Biographies

Peter Adebayo Idowu, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
Department of Computer Science and Engineering, Faculty of Technology,
Jeremiah Ademola Balogun, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
Department of Computer Science and Engineering, Faculty of Technology,
Adeniran Ishola Oluwaranti, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria
Department of Computer Science and Engineering, Faculty of Technology

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Published
2015-05-02