Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Dal Moro, F; Abate, A; Lanckriet, GR; Arandjelovic, G; Gasparella, P; Bassi, P; Mancini, M; Pagano, F.
A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines.
Kidney Int. 2006; 69(1): 157-160.
Doi: 10.1038/sj.ki.5000010
Web of Science
PubMed
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- Co-Autor*innen der Med Uni Graz
- Gasparella Paolo
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- Abstract:
- 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Algorithms -
- Artificial Intelligence -
- Humans -
- Logistic Models -
- Neural Networks, Computer -
- Ureteral Calculi - epidemiology
- Find related publications in this database (Keywords)
- urolithiasis
- ureteral calculi
- support vector machine
- artificial intelligence
- statistical methods
- neural networks