:: Volume 4, Issue 4 (winter 2018) ::
2018, 4(4): 266-278 Back to browse issues page
Modeling Breast Cancer Using Data Mining Methods
Parvaneh Dehghan, Maedeh Mogharabi, Iman Zabbah , Kamran Layeghi, Ali Maroosi
PhD Student, School of Electrical and Computer, Tehran North Branch, Islamic Azad University, Tehran, Iran.
Abstract:   (4200 Views)
Introduction: Breast cancer is the most common form of cancer in women. Breast cancer detection is considered as one of the most important issues in medical science. Diagnosis of benign or malignant type of cancer reduces costs and also is important in deciding about the treatment strategy. The aim of this study was to provide data mining based models that have the predictability of breast cancer detection.
Methods: This study was descriptive-analytic. Its database included 683 independent records containing nine clinical variables in the UCI machine learning. Multilayer Perceptron artificial neural network, Bayesian Neural Network and LVQ neural network were used for classification of breast cancer to benign and malignant types. In this study, 80% of data were used for network training and 20% were used for testing.
Results: After pre-processing the data, different neural networks with different architectures were used to detect breast cancer. In the best condition, we could predict benign or malignant cancer in the MLP neural networks, LVQ and Bayesian Neural Networks with an average of ten tests with an accuracy of 97.5% and 97.6% and 98.3% respectively. Our investigations showed that Bayesian neural network had a better performance.
Conclusion: Breast cancer is one of the most common cancers among women. Early diagnosis of disease reduces healthcare costs and increases patient survival chance. In this study, using data mining techniques in diagnosis, the researchers were able to use Bayesian neural network to achieve high accuracy in diagnosis.
Keywords: Breast Cancer, Neural Networks, LVQ, Data Mining
Full-Text [PDF 1182 kb]   (2107 Downloads)    
Type of Study: Original Article | Subject: Data Mining
Received: 2017/07/14 | Accepted: 2017/11/26

XML   Persian Abstract   Print

Volume 4, Issue 4 (winter 2018) Back to browse issues page