Review: Using support vector machines in diagnoses of urological dysfunctions

Authors:
David Gil;Magnus Johnsson
Affiliations:
Computing Technology and Data Processing, University of Alicante, Spain;Lund University Cognitive Science, Sweden
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

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%.