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DOI: 10.14569/IJACSA.2020.0110808
PDF

Predicting Breast Cancer via Supervised Machine Learning Methods on Class Imbalanced Data

Author 1: Keerthana Rajendran
Author 2: Manoj Jayabalan
Author 3: Vinesh Thiruchelvam

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 8, 2020.

  • Abstract and Keywords
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Abstract: A widespread global health concern among women is the incidence of the second most leading cause of fatality which is breast cancer. Predicting the occurrence of breast cancer based on the risk factors will pave the way to an early diagnosis and an efficient treatment in a quicker time. Although there are many predictive models developed for breast cancer in the past, most of these models are generated from highly imbalanced data. The imbalanced data is usually biased towards the majority class but in cancer diagnosis, it is crucial to diagnose the patients with cancer correctly which are oftentimes the minority class. This study attempts to apply three different class balancing techniques namely oversampling (Synthetic Minority Oversampling Technique (SMOTE)), undersampling (SpreadSubsample) and a hybrid method (SMOTE and SpreadSubsample) on the Breast Cancer Surveillance Consortium (BCSC) dataset before constructing the supervised learning methods. The algorithms employed in this study include Naïve Bayes, Bayesian Network, Random Forest and Decision Tree (C4.5). The balancing method which yields the best performance across all the four classifiers were tested using the validation data to determine the final predictive model. The performances of the classifiers were evaluated using a Receiver Operating Characteristic (ROC) curve, sensitivity, and specificity.

Keywords: Breast cancer; class imbalance; diagnosis; bayesian network

Keerthana Rajendran, Manoj Jayabalan and Vinesh Thiruchelvam, “Predicting Breast Cancer via Supervised Machine Learning Methods on Class Imbalanced Data” International Journal of Advanced Computer Science and Applications(IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110808

@article{Rajendran2020,
title = {Predicting Breast Cancer via Supervised Machine Learning Methods on Class Imbalanced Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110808},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110808},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {8},
author = {Keerthana Rajendran and Manoj Jayabalan and Vinesh Thiruchelvam}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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