Abstract: This paper proposes an automatic diagnostic method for breast tumour disease using hybrid Support Vector Machine (SVM) and the Two-Step Clustering Technique. The hybrid technique is aimed at improving the diagnostic accuracy and reducing diagnostic miss-classification, thereby solving the classification problems related to Breast Tumour. To distinguish the hidden patterns of the malignant and benign tumours, the Two-Step algorithm and SVM have been combined and employed to differentiate the incoming tumours. The developed hybrid method enhances the accuracy by 99.1% when examined on the UCI-WBC data set. Moreover, in terms of evaluation measures, it has been shown experimentally results that the hybrid method outperforms the modern classification techniques for breast cancer diagnosis.

Ahmed Hamza Osman. “An Enhanced Breast Cancer Diagnosis Scheme based on Two-Step-SVM Technique”. International Journal of Advanced Computer Science and Applications (IJACSA) 8.4 (2017). http://dx.doi.org/10.14569/IJACSA.2017.080423

@article{Osman2017,
title = {An Enhanced Breast Cancer Diagnosis Scheme based on Two-Step-SVM Technique},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080423},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080423},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {4},
author = {Ahmed Hamza Osman}
}