A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines.
The objective of this study was to optimally predict the spontaneous passage of ureteral stones in patients with renal colic by applying for the first time support vector machines (SVM), an instance of kernel methods, for classification. After reviewing the results found in the literature, we compar...
| Publicado en: | Kidney International Vol. 69; no. 1; pp. 157 - 161 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Jan2006
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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=106259262&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106259262 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00852538 4CU jtl: Kidney International issn: 00852538 maglogo: N pubinfo: dt: Jan2006 vid: 69 iid: 1 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 106259262 2009299347 10.1038/sj.ki.5000010 NLM16374437 106259262 ppf: 157 ppct: 4 formats: tig: atl: A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines. aug: au: Dal Moro F Abate A Lanckriet GRG Arandjelovic G Gasparella P Bassi P Mancini M Pagano F affil: Department of Urology, University of Padova, Padova, Italy. fabrizio.dalmoro@unipd.it sug: subj: Ureteral Calculi Algorithms Fisher's Exact Test Italy Logistic Regression Models, Statistical Patient Selection Sensitivity and Specificity Human ab: The objective of this study was to optimally predict the spontaneous passage of ureteral stones in patients with renal colic by applying for the first time support vector machines (SVM), an instance of kernel methods, for classification. After reviewing the results found in the literature, we compared the performances obtained with logistic regression (LR) and accurately trained artificial neural networks (ANN) to those obtained with SVM, that is, the standard SVM, and the linear programming SVM (LP-SVM); the latter techniques show an improved performance. Moreover, we rank the prediction factors according to their importance using Fisher scores and the LP-SVM feature weights. A data set of 1163 patients affected by renal colic has been analyzed and restricted to single out a statistically coherent subset of 402 patients. Nine clinical factors are used as inputs for the classification algorithms, to predict one binary output. The algorithms are cross-validated by training and testing on randomly selected train- and test-set partitions of the data and reporting the average performance on the test sets. The SVM-based approaches obtained a sensitivity of 84.5% and a specificity of 86.9%. The feature ranking based on LP-SVM gives the highest importance to stone size, stone position and symptom duration before check-up. We propose a statistically correct way of employing LR, ANN and SVM for the prediction of spontaneous passage of ureteral stones in patients with renal colic. SVM outperformed ANN, as well as LR. This study will soon be translated into a practical software toolbox for actual clinical usage.Kidney International (2006) 69, 157–160. doi:10.1038/sj.ki.5000010 pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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