Performance Analysis of Twin-Support Vector Machine in Breast Cancer Prediction

Abstract

Breast cancer has become a major leading cause of death and incapacity worldwide. Recently, breast cancer is being responsible for a huge number of deaths of the female gender. In this study, we have implemented the Twin-Support Vector Machine (TW-SVM) to illustrate the power of machine learning techniques. TW-SVM is a recently developed algorithm and yet it is very powerful. For performance measurement, a competitive comparison between the proposed TW-SVM and SVM classifiers has been done based on the WDBC dataset. The results showed that TW-SVM can provide promising performance rates. It outperformed the SVM algorithm as well as other existing works by achieving the highest accuracy of 99.11% for predicting the considered disease.