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 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.

[1]  M. Pearle Efficacy of tamsulosin in the medical management of juxtavesical ureteral stones. , 2004, International braz j urol : official journal of the Brazilian Society of Urology.

[2]  B. Shuckett,et al.  Artificial neural networks in pediatric urology: prediction of sonographic outcome following pyeloplasty. , 1998, The Journal of urology.

[3]  Michael M. Leane,et al.  The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets , 2003, AAPS PharmSciTech.

[4]  J V Tu,et al.  Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.

[5]  M. Moran,et al.  A computer model to predict the outcome and duration of ureteral or renal calculous passage. , 2004, The Journal of urology.

[6]  C. Kane,et al.  Time to stone passage for observed ureteral calculi: a guide for patient education. , 1999, The Journal of urology.

[7]  A W Partin,et al.  Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer. , 2001, Urology.

[8]  C. Türk,et al.  Management of ureteric stones. , 1994, European urology.

[9]  S. Izenberg,et al.  Prediction of spontaneous ureteral calculous passage by an artificial neural network. , 2000, The Journal of urology.

[10]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[11]  J. Wolf Nifedipine versus tamsulosin for the management of lower ureteral stones. , 2004, International braz j urol : official journal of the Brazilian Society of Urology.

[12]  Mark J. Warshawsky,et al.  A Modern Approach , 2005 .

[13]  M. Gomha,et al.  Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model? , 2004, The Journal of urology.

[14]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[15]  Paul S. Bradley,et al.  Feature Selection via Mathematical Programming , 1997, INFORMS J. Comput..

[16]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[17]  Daniel Khashabi Support-Vector Machines and Kernel Methods , 2021, Computer Age Statistical Inference, Student Edition.

[18]  J. Lingeman,et al.  Ureteral Stones Clinical Guidelines Panel summary report on the management of ureteral calculi. The American Urological Association. , 1997, The Journal of urology.

[19]  Theodore Anagnostou,et al.  Management of ureteric stones. , 2004, European urology.