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Statistical Pattern Recognition Techniques for Early Diagnosis of Diabetic Neuropathy by Posturographic Data
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Author(s): Claudia Diamantini (Università Politecnica delle Marche, Italy), Sandro Fioretti (Università Politecnica delle Marche, Italy)and Domenico Potena (Università Politecnica delle Marche, Italy)
Copyright: 2012
Pages: 12
Source title:
Medical Applications of Intelligent Data Analysis: Research Advancements
Source Author(s)/Editor(s): Rafael Magdalena-Benedito (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Emilio Soria-Olivas (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Guerrero Martínez (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Gómez-Sanchis (Intelligent Data Analysis Laboratory, University of Valencia, Spain)and Antonio Jose Serrano-López (Intelligent Data Analysis Laboratory, University of Valencia, Spain)
DOI: 10.4018/978-1-4666-1803-9.ch002
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Abstract
The goal of this chapter is to describe the use of statistical pattern recognition techniques in order to build a classification model for the early diagnosis of peripheral diabetic neuropathy. In particular, the authors present two experimental methodologies, based on linear discriminant analysis and Bayes vector quantizer algorithms respectively. The former algorithm has demonstrated the best performance in distinguish between non-neuropathic and neuropathic patients, while the latter is able to build models that recognize the severity of the neuropathy.
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