Abstract

Info

Author(s):: LAI Shengsheng1; LIU Qiancheng1; YU Liling1; LIUWenping1; YANG Ruimeng2; JIN Haoyu1; 1. School of Medical Devices, Guangdong Food and Drug Vocational College, Guangzhou 510520, China; 2. Department of Radiology, the Second Affiliated Hospital of South China University of Technology, Guangzhou First People’s Hospital, Guangzhou 510180, China

Keywords:: Keywords: breast cancer; prediction model; sequential forward feature selection algorithm; support vector machine algorithm

Abstract:: Abstract: Objective To improve the accuracy of computer-aided diagnosis for fine needle aspiration pathology in breast cancer by employing the breast cancer prediction model based on sequential forward feature selection (SFS) algorithm and support vector machine (SVM) classifier. Methods The pathological data of 456 breast tumors were used as training set. A total of 30 features were screened by SFS-SVM algorithm to obtain the optimal feature combination, and then the pathological data of 112 breast tumors were used as test set to construct breast cancer prediction model. The prediction accuracy of the constructed model was evaluated with 5-fold cross-validation method. The evaluation indicators included area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results Compared with SVM-based model which had an AUC of 97.00% and an accuracy of 92.42%, the breast cancer prediction model based on SFS-SVM had better performances, achieving an AUC of 98.39% and an accuracy of 97.35%. Conclusion The breast cancer prediction model based on SFS-SVM exhibits a good predictive efficacy on the auxiliary diagnosis of breast cancers.