Optimal breast cancer classification using Gauss–Newton representation based algorithm

• Published in: Expert systems with applications Vol. 85; pp. 134 - 145
• Main Authors: Dora, Lingraj, Agrawal, Sanjay, Panda, Rutuparna, Abraham, Ajith
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.11.2017; Elsevier BV
• Subjects: Accuracy; Algorithms; Artificial intelligence; Breast cancer; Breast cancer classification; Cancer; Classification; Diagnostic systems; Euclidean distance measure; Expert systems; Gauss-Newton representation based algorithm; Machine learning; Pattern recognition; Sparse representation; Statistical analysis; Statistical methods; Training; Womens health; Wisconsin; Sparse representation; Breast cancer classification; Gauss-Newton representation based algorithm; Euclidean distance measure
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.05.035

•A novel GNRBA for breast cancer classification is proposed.•It uses sparse representation with feature selection.•It evaluates the sparsity in a computationally efficient way.•A new Gauss-Newton based classifier is proposed.•Experimental results on two databases of WBCD are presented. Breast cancer is a decisive disease worldwide. It is one of the most widely spread cancer among women. As per the survey, one out of eight women in the world are at risk of breast cancer at some point of time in her life. One of the methods to reduce breast cancer mortality rate is timely detection and effective treatment. That is why, more accurate classification of a breast cancer tumor has become a challenging problem in the medical field. Many classification techniques are proposed in the literature. Today, expert systems and machine learning techniques are being extensively used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we have proposed a novel Gauss-Newton representation based algorithm (GNRBA) for breast cancer classification. It uses the sparse representation with training sample selection. Until now, sparse representation has been successfully applied in pattern recognition only. The proposed method introduces a novel Gauss-Newton based approach to find the optimal weights for the training samples for classification. In addition, it evaluates the sparsity in a computationally efficient way as compared to the conventional l1-norm method. The effectiveness of the GNRBA is examined on the Wisconsin Breast Cancer Database (WBCD) and the Wisconsin Diagnosis Breast Cancer (WDBC) database from the UCI Machine Learning repository. Various performance measures like classification accuracy, sensitivity, specificity, confusion matrices, a statistical test and the area under the receiver operating characteristic (AUC) are reported to show the superiority of the proposed method as compared to classical models. The experimental results show that the proposed GNRBA could be a good alternative for breast cancer classification for clinical experts.