A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis.
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...
| Publicado en: | Artificial Intelligence in Medicine Vol. 77; pp. 31 - 48 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Mar2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123173959&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123173959 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Mar2017 vid: 77 pid: 1004 pub: Elsevier B.V. artinfo: ui: 123173959 123173959 NLM28545610 123173959 10.1016/j.artmed.2017.02.003 NLM28545610 123173959 ppf: 31 ppct: 17 formats: tig: atl: A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis. aug: au: Gu, Dongxiao Liang, Changyong Zhao, Huimin affil: School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui, 230009, China sug: subj: Decision Support Techniques Breast Neoplasms Expert Systems Female ROC Curve Algorithms Software Human Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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