Using support vector machines in diagnoses of urological dysfunctions

Urinary incontinence is one of the largest diseases affecting between 10% and 30% of the adult population and an increase is expected in the next decade with rising treatment costs as a consequence. There are many types of urological dysfunctions causing urinary incontinence, which makes cheap and accurate diagnosing an important issue. This paper proposes a support vector machine (SVM) based method for diagnosing urological dysfunctions. 381 registers collected from patients suffering from a variety of urological dysfunctions have been used to ensure the (generalization) performance of the decision support system. Moreover, the robustness of the proposed system is examined by fivefold cross-validation and the results show that the SVM-based method can achieve an average classification accuracy at 84.25%.

[1]  A. Wein,et al.  Prediction of Genitourinary Tract Morbidity After Brachytherapy for Prostate Adenocarcinoma , 2004 .

[2]  Lipo Wang,et al.  Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[3]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[4]  Antonio Soriano Payá,et al.  Embedded system for diagnosing dysfunctions in the lower urinary tract , 2007, SAC '07.

[5]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[6]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[7]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[8]  C. Kelleher,et al.  Costs of female urinary incontinence , 2005 .

[9]  Ingoo Han,et al.  Hybrid genetic algorithms and support vector machines for bankruptcy prediction , 2006, Expert Syst. Appl..

[10]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

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

[12]  K. Coyne,et al.  Population-based survey of urinary incontinence, overactive bladder, and other lower urinary tract symptoms in five countries: results of the EPIC study. , 2006, European urology.

[13]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[14]  Hongmei Yan,et al.  SVM-based decision support system for clinic aided tracheal intubation predication with multiple features , 2009, Expert Syst. Appl..

[15]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[16]  O. Mangasarian,et al.  Robust linear programming discrimination of two linearly inseparable sets , 1992 .

[17]  Bernhard Schölkopf,et al.  Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators , 1998 .

[18]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[19]  David Gil Méndez,et al.  Application of artificial neural networks in the diagnosis of urological dysfunctions , 2009, Expert Syst. Appl..

[20]  Özge Uncu,et al.  A novel feature selection approach: Combining feature wrappers and filters , 2007, Inf. Sci..

[21]  Kyoung-jae Kim,et al.  Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting , 2004, Applied Intelligence.

[22]  Shian-Chang Huang,et al.  Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities , 2008, Expert Syst. Appl..

[23]  Zhong-Fu Wu,et al.  Efficient feature selection for high-dimensional data using two-level filter , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[24]  Wlodzislaw Duch.  Filter methods , 2004 .

[25]  Mehmet Fatih Akay,et al.  Support vector machines combined with feature selection for breast cancer diagnosis , 2009, Expert Syst. Appl..

[26]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

[27]  S. Hua,et al.  A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. , 2001, Journal of molecular biology.

[28]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[29]  Sanmay Das,et al.  Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection , 2001, ICML.

[30]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[31]  Dr. M. G. Worster.  Methods of Mathematical Physics , 1947, Nature.

[32]  Michael I. Jordan,et al.  Feature selection for high-dimensional genomic microarray data , 2001, ICML.

[33]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[34]  Teh-Wei Hu,et al.  Costs of urinary incontinence and overactive bladder in the United States: a comparative study. , 2004, Urology.

[35]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[36]  Shian-Chang Huang,et al.  A Hybrid Unscented Kalman Filter and Support Vector Machine Model in Option Price Forecasting , 2006, ICNC.

[37]  C. Goose,et al.  Glossary of Terms , 2004, Machine Learning.

[38]  Yi Pan,et al.  Transmembrane segments prediction and understanding using support vector machine and decision tree , 2006, Expert Syst. Appl..

[39]  Jitendra Malik,et al.  Learning to Detect Natural Image Boundaries Using Brightness and Texture , 2002, NIPS.