A novel intelligent classification model for breast cancer diagnosis

• Published in: Information processing & management 2019-05, Vol.56 (3), p.609-623
• Main Authors: Liu, Na, Qi, Er-Shi, Xu, Man, Gao, Bo, Liu, Gui-Qiu
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Breast cancer; Breast cancer diagnosis; Classification; Clinical research; Computer simulation; Cost-sensitive learning; Detection; Diagnosis; Early warnings; Efficacy; False positive results; Feature selection; Genetic algorithm; Immunoglobulins; Information gain; Intelligence; Learning algorithms; Medical diagnosis; Medical personnel; Ratings & rankings; Studies; Support vector machine; Women
• ISSN: 0306-4573; 0166-0462; 1873-5371; 1879-2308
• DOI: 10.1016/j.ipm.2018.10.014

•A novel intelligent classification method for breast cancer diagnosis is proposed.•The novel method has considered the misclassification cost of the breast cancer tumor.•The novel method called “IGSAGAW-CSSVM”, which applied IGSAGAW for feature selection and CSSVM perform for breast cancer classification.•The classification results by our proposed method, Genetic Algorithm Wrapper and Baseline classification models are compared for WDBC and WBC data sets. Breast cancer is one of the leading causes of death among women worldwide. Accurate and early detection of breast cancer can ensure long-term surviving for the patients. However, traditional classification algorithms usually aim only to maximize the classification accuracy, failing to take into consideration the misclassification costs between different categories. Furthermore, the costs associated with missing a cancer case (false negative) are clearly much higher than those of mislabeling a benign one (false positive). To overcome this drawback and further improving the classification accuracy of the breast cancer diagnosis, in this work, a novel breast cancer intelligent diagnosis approach has been proposed, which employed information gain directed simulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection, in this process, we performs the ranking of features according to IG algorithm, and extracting the top m optimal feature utilized the cost sensitive support vector machine (CSSVM) learning algorithm. Our proposed feature selection approach which can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but it can also obtain the maximum classification accuracy and minimum misclassification cost. The efficacy of our proposed approach is tested on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer data sets, and the results demonstrate that our proposed hybrid algorithm outperforms other comparison methods. The main objective of this study was to apply our research in real clinical diagnostic system and thereby assist clinical physicians in making correct and effective decisions in the future. Moreover our proposed method could also be applied to other illness diagnosis.