Dongxiao Gu1, Changyong Liang2, Huimin Zhao3. 1. School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui, 230009, China. Electronic address: gudongxiao@hfut.edu.cn. 2. School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui, 230009, China. Electronic address: cyliang@hfut.edu.cn. 3. Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, 3202 North Maryland Avenue, Milwaukee, WI, 53201, USA. Electronic address: hzhao@uwm.edu.
Abstract
OBJECTIVE: We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making. METHODS: We observed two problems in applying standard CBR to this context: the abundance of different types of attributes and the difficulty in eliciting appropriate attribute weights from human experts. We therefore used a distance measure named weighted heterogeneous value distance metric, which can better deal with both continuous and discrete attributes simultaneously than the standard Euclidean distance, and a genetic algorithm for learning the attribute weights involved in this distance measure automatically. We evaluated our CBR system in two case studies, related to benign/malignant tumor prediction and secondary cancer prediction, respectively. RESULT: Weighted heterogeneous value distance metric with genetic algorithm for weight learning outperformed several alternative attribute matching methods and several classification methods by at least 3.4%, reaching 0.938, 0.883, 0.933, and 0.984 in the first case study, and 0.927, 0.842, 0.939, and 0.989 in the second case study, in terms of accuracy, sensitivity×specificity, F measure, and area under the receiver operating characteristic curve, respectively. CONCLUSION: The evaluation result indicates the potential of CBR in the breast cancer diagnosis domain.
OBJECTIVE: We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making. METHODS: We observed two problems in applying standard CBR to this context: the abundance of different types of attributes and the difficulty in eliciting appropriate attribute weights from human experts. We therefore used a distance measure named weighted heterogeneous value distance metric, which can better deal with both continuous and discrete attributes simultaneously than the standard Euclidean distance, and a genetic algorithm for learning the attribute weights involved in this distance measure automatically. We evaluated our CBR system in two case studies, related to benign/malignant tumor prediction and secondary cancer prediction, respectively. RESULT: Weighted heterogeneous value distance metric with genetic algorithm for weight learning outperformed several alternative attribute matching methods and several classification methods by at least 3.4%, reaching 0.938, 0.883, 0.933, and 0.984 in the first case study, and 0.927, 0.842, 0.939, and 0.989 in the second case study, in terms of accuracy, sensitivity×specificity, F measure, and area under the receiver operating characteristic curve, respectively. CONCLUSION: The evaluation result indicates the potential of CBR in the breast cancer diagnosis domain.
Authors: Yating Zhao; Changyong Liang; Zuozuo Gu; Yunjun Zheng; Qilin Wu Journal: Int J Environ Res Public Health Date: 2020-04-24 Impact factor: 3.390
Authors: Aida Khakimova; Xuejie Yang; Oleg Zolotarev; Maria Berberova; Michael Charnine Journal: Int J Environ Res Public Health Date: 2020-10-13 Impact factor: 3.390