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ISSN: 2157-7420

Journal of Health & Medical Informatics
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  • Research Article   
  • J Health Med Inform 2013, Vol 4(2): 124
  • DOI: 10.4172/2157-7420.1000124

Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence

Ahmad LG*, Eshlaghy AT, Poorebrahimi A, Ebrahimi M and Razavi AR
Department of Management Information Systems, Science and Research Branch, Islamic Azad University of Tehran-Iran, , Iran
*Corresponding Author : Ahmad LG, Department of Management Information Systems, Science and Research Branch, Islamic Azad University Of Tehran-Iran, Iran, Email: [email protected]

Received Date: Jan 28, 2013 / Accepted Date: Apr 18, 2013 / Published Date: Apr 24, 2013

Abstract

Objective: The number and size of medical databases are increasing rapidly but most of these data are not analyzed
for finding the valuable and hidden knowledge. Advanced data mining techniques can be used to discover hidden
patterns and relationships. Models developed from these techniques are useful for medical practitioners to make right
decisions. The present research studied the application of data mining techniques to develop predictive models for
breast cancer recurrence in patients who were followed-up for two years.
Method: The patients were registered in the Iranian Center for Breast Cancer (ICBC) program from 1997 to 2008.
The dataset contained 1189 records, 22 predictor variables, and one outcome variable. We implemented machine
learning techniques, i.e., Decision Tree (C4.5), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to
develop the predictive models. The main goal of this paper is to compare the performance of these three well-known
algorithms on our data through sensitivity, specificity, and accuracy.
Results and Conclusion: Our analysis shows that accuracy of DT, ANN and SVM are 0.936, 0.947 and 0.957
respectively. The SVM classification model predicts breast cancer recurrence with least error rate and highest accuracy.
The predicted accuracy of the DT model is the lowest of all. The results are achieved using 10-fold cross-validation for
measuring the unbiased prediction accuracy of each model.

Keywords: Artificial neural networks; Breast cancer recurrence; Classification; Decision tree; Machine learning; Support vector machine; 10-Fold cross-validation

Citation: Ahmad LG, Eshlaghy AT, Poorebrahimi A, Ebrahimi M, Razavi AR (2013) Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence. J Health Med Inform 4:124. Doi: 10.4172/2157-7420.1000124

Copyright: © 2013 Ahmad LG, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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