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

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.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Gu D,Liang C,Zhao H

doi

10.1016/j.artmed.2017.02.003

subject

Has Abstract

pub_date

2017-03-01 00:00:00

pages

31-47

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(16)30163-4

journal_volume

77

pub_type

杂志文章

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