Performance Comparison of SVM and its Variants for the Early Prognosis of Breast Cancer


  • Talha Ahmed Khan British Malaysian Institute (BMI), Universiti Kuala Lumpur, Malaysia and Usman Institute of Technology, Karachi, Pakistan
  • Muhammad Alam CCIS, Institute of Business Management, Karachi, Pakistan and Malaysian Institute of Information and Technology (MIIT), Universiti Kuala Lumpur Malaysia
  • Zeeshan Shahi Electrical Engineering Department, Institute of Business Management, Karachi, Pakistan
  • M.S. Mazliham Malaysian France Institute (MFI), Universiti Kuala Lumpur Malaysia



Benign, Malignant, Breast cancer, dense breast, fatty breast, Support Vector Machine


Breast cancer has become a leading cause of women death in this era. Breast cancer is very common in various countries including Pakistan. Early identification of the breast cancer or tumor is the only way for the rapid treatment and cure. An imaging approach named as mammography has performed tremendous job in the field of medical to detect the cancer tumors on early basis with less false alarm rate. Breast cancer has two types of tumors a) Benign and b) Malignant. Malignant is acknowledged as cancer tumor as it spread and grow rapidly inside the tissues. Detection of Malignant tumor is very complex in dense breast as it is covered and linked with the milk glands, ducts and other related tissues. Therefore, machine learning and artificial intelligence approaches were needed as mammographic images required edge detection, image enhancement and image processing. Various Artificial Intelligence based algorithms have been applied to the clinical breast cancer data set for the early detection of breast tumor. In this research work the clinical data has been collected from the UCI machine learning repository for the classification of breast cancer tumor a) Benign and b) Malignant. Support Vector machine with its variants Kernel, Gaussian Kernel and Sigmoid Kernel have been applied to the linearly separable breast cancer data set for comparative analysis. Results proved that all the variants of SVM performed better for the breast cancer classification. 


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