A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis

Publication date: Available online 11 February 2017
Source:Artificial Intelligence in Medicine
Author(s): Dongxiao Gu, Changyong Liang, Huimin Zhao
Currently, breast cancer diagnosis depends largely on physicians' experiential knowledge. 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. In this paper, we present the implementation of a case-based reasoning (CBR) system for breast cancer related diagnoses and its application in two studies related to benign/malignant tumor prediction and secondary cancer prediction, respectively. 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. Our evaluation based on two real-world breast cancer data sets indicates the potential of CBR in the breast cancer diagnosis domain.



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