An effective approach for breast cancer diagnosis based on routine blood analysis features.
Breast cancer is a widespread disease and one of the primary causes of cancer mortality among women all over the world. Computer-aided methods are used to assist medical doctors to make early diagnosis of the disease. The aim of this study is to build an effective prediction model for breast cancer...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 7; pp. 1583 - 1602 |
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| Autores principales: | , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2020
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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=143819696&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143819696 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2020 vid: 58 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143819696 143819696 143951786 NLM32436139 143819696 10.1007/s11517-020-02187-9 NLM32436139 143819696 ppf: 1583 ppct: 19 formats: fmt: @attributes: type: P tig: atl: An effective approach for breast cancer diagnosis based on routine blood analysis features. aug: au: Yavuz, Erdem Eyupoglu, Can affil: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, Yildirim, Bursa, Turkey sug: subj: Breast Neoplasms Blood Diagnosis, Computer Assisted Methods Breast Neoplasms Diagnosis Female Resource Databases Factor Analysis Software Algorithms Female ab: Breast cancer is a widespread disease and one of the primary causes of cancer mortality among women all over the world. Computer-aided methods are used to assist medical doctors to make early diagnosis of the disease. The aim of this study is to build an effective prediction model for breast cancer diagnosis based on anthropometric data and parameters collected through routine blood analysis. The proposed approach innovatively exploits principal component analysis (PCA) technique cascaded by median filtering so as to transform original features into a form of containing less distractive noise not to cause overfitting. Since a generalized regression neural network (GRNN) model is adopted to classify patterns of the transformed features, the computational load imposed in the training of artificial neural network model is kept minimized thanks to the non-iterative nature of GRNN training. The proposed method has been devised and tested on the recent Breast Cancer Coimbra Dataset (BCCD) that contains 9 clinical features measured for each of 116 subjects. Outperforming all of the existing studies on BCCD, our method achieved a mean accuracy rate of 0.9773. Experimental results evidence that this study achieves the best prediction performance ever reported on this dataset. The fact that our proposed approach has accomplished such a boosted performance of breast cancer diagnosis based on routine blood analysis features offers a great potential to be used in a widespread manner to detect the disease in its inception phase. Graphical abstract. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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