Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization

Three contributions are proposed. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which is a combination between the Harris hawks optimization (HHO) and a support vector machine (SVM) is introduced. Second, the performance of the HHO-SVM is enhanced using the conventional normalization method. The final contribution is to improve the efficiency of the HHO-SVM by adopting a parallel approach that employs the data distribution. The proposed models are evaluated using the Wisconsin Diagnosis Breast Cancer (WDBC) dataset. The results show that the HHO-SVM achieves a 98.24% accuracy rate with the normalization scaling technique, outperforming other related works. On the other hand, the HHO-SVM achieves a 99.47% accuracy rate with the equilibration scaling technique, which is better than other previous works. Finally, to compare the three effective scaling strategies on four CPU cores, the parallel version of the proposed model provides an acceleration of 3.97.

Automated summary

Three contributions are proposed in this study. Firstly, a novel hybrid classifier (HHO-SVM) is introduced, which combines the Harris hawks optimization (HHO) and a support vector machine (SVM). Secondly, the performance of the HHO-SVM is improved using the conventional normalization method. Lastly, a parallel approach is adopted to enhance the efficiency of the HHO-SVM by utilizing data distribution. Evaluation on the Wisconsin Diagnosis Breast Cancer (WDBC) dataset shows that the HHO-SVM achieves high accuracy rates with different scaling techniques and the parallel version provides significant acceleration.