Evaluation of Feature Selection Methods for Mammographic Breast Cancer Diagnosis in a Unified Framework.
Over recent years, feature selection (FS) has gained more attention in intelligent diagnosis. This study is aimed at evaluating FS methods in a unified framework for mammographic breast cancer diagnosis. After FS methods generated rank lists according to feature importance, the framework added featu...
| Publicado en: | BioMed Research International pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/4/2021
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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=152795430&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152795430 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/4/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 152795430 152795430 152795430 10.1155/2021/6079163 152795430 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluation of Feature Selection Methods for Mammographic Breast Cancer Diagnosis in a Unified Framework. aug: au: Tian, Chun-jiang Lv, Jian Xu, Xiang-feng affil: Department of Radiology, Tianjin Hospital of ITCWM Nankai Hospital, Tianjin 300100, China sug: subj: Breast Neoplasms Diagnosis Mammography Methods Breast Pathology Conceptual Framework Human Female Random Forest Breast Neoplasms Classification Health Screening Sensitivity and Specificity Experimental Studies Wilcoxon Rank Sum Test Female ab: Over recent years, feature selection (FS) has gained more attention in intelligent diagnosis. This study is aimed at evaluating FS methods in a unified framework for mammographic breast cancer diagnosis. After FS methods generated rank lists according to feature importance, the framework added features incrementally as the input of random forest which performed as the classifier for breast lesion classification. In this study, 10 FS methods were evaluated and the digital database for screening mammography (1104 benign and 980 malignant lesions) was analyzed. The classification performance was quantified with the area under the curve (AUC), and accuracy, sensitivity, and specificity were also considered. Experimental results suggested that both infinite latent FS method (AUC, 0.866 ± 0.028) and RELIEFF (AUC, 0.855 ± 0.020) achieved good prediction (AUC ≥ 0.85) when 6 features were used, followed by correlation-based FS method (AUC, 0.867 ± 0.023) using 7 features and WILCOXON (AUC, 0.887 ± 0.019) using 8 features. The reliability of the diagnosis models was also verified, indicating that correlation-based FS method was generally superior over other methods. Identification of discriminative features among high-throughput ones remains an unavoidable challenge in intelligent diagnosis, and extra efforts should be made toward accurate and efficient feature selection. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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