Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine.
Early diagnosis and treatment are the most important strategies to prevent deaths from several diseases. In this regard, data mining and machine learning techniques have been useful tools to help minimize errors and to provide useful information for diagnosis. Our paper aims to present a new feature...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 3; pp. 519 - 529 |
|---|---|
| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2020
|
| 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=142105117&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142105117 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2020 vid: 58 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142105117 142105117 NLM31900818 142105117 10.1007/s11517-019-02100-z NLM31900818 142105117 ppf: 519 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine. aug: au: de Lima, Márcio Dias de Oliveira Roque e Lima, Juliana Barbosa, Rommel M. affil: Instituto Federal de Educação, Ciência e Tecnologia de Goiás, R. 75 - St. Central, CEP 74055-110, Goiânia, GO, Brazil sug: subj: Databases Female Human Female ab: Early diagnosis and treatment are the most important strategies to prevent deaths from several diseases. In this regard, data mining and machine learning techniques have been useful tools to help minimize errors and to provide useful information for diagnosis. Our paper aims to present a new feature selection algorithm. In order to validate our study, we used eight benchmark data sets which are commonly used among researchers who developed machine learning methods for medical data classification. The experiment has shown that the performance of our proposed new feature selection method combined with twin-bounded support vector machine (FSTBSVM) is very efficient. The robustness of the FSTBSVM is examined using classification accuracy, analysis of sensitivity, and specificity. The proposed FSTBSVM is a very promising technique for classification, and the results show that the proposed method is capable of producing good results with fewer features than the original data sets. Graphical abstract Model using a new feature selection and grid search with 10-fold CV to optimize model parameters in our FSTBSVM. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|