Effective kernel-principal component analysis based approach for wisconsin breast cancer diagnosis

This work aims to identify cancerous (malignant) and non-malignant (non-cancerous) cells in a breast cancer database. Wisconsin breast cancer data (WBC) was utilized and obtained from the University of California, Irvine's machine learning repository. The proposed approach involves the Naive bayes algorithm with Gaussian distribution of the function in combination with Chi-squared-based attributes selection approach. This experimentation has been done after reducing the dimensional space of the used data with extended Kernel Principal Component Analysis (K-PCA). Five different kernels in K-PCA have been tested after the implementation of necessary pre-processing techniques. The performance assessment of the proposed system has been evaluated based on confusion matrix-based accuracy, precision, sensitivity, and specificity. Our proposed methodology with six selected feature and sigmoid K-PCA attained the best accuracy of 99.28%. This result outer performs many state-of-the-art studies recently published on the identical dataset.

Automated summary

This study aims to distinguish between malignant and non-malignant cells in a breast cancer database. The Wisconsin breast cancer data was used, and the Naive Bayes algorithm with Gaussian distribution in combination with Chi-squared-based attribute selection was proposed. The data dimension was reduced using extended Kernel Principal Component Analysis (K-PCA), and five different kernels were tested. The performance of the proposed system was evaluated based on accuracy, precision, sensitivity, and specificity, achieving a best accuracy of 99.28% with six selected features and sigmoid K-PCA. This outperforms many recent state-of-the-art studies on the same dataset.