Statistical Pattern Recognition Techniques for Early Diagnosis of Diabetic Neuropathy by Posturographic Data
Title | Statistical Pattern Recognition Techniques for Early Diagnosis of Diabetic Neuropathy by Posturographic Data |
Publication Type | Conference Proceedings |
Year of Conference | 2012 |
Authors | Diamantini C, Fioretti, S., Potena, D |
Conference Name | MEDICAL APPLICATIONS OF INTELLIGENT DATA ANALYSIS: RESEARCH ADVANCEMENTS |
Pagination | 17 - 28 |
Date Published | 2012 |
Publisher | IGI GLOBAL |
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 vec- tor 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. |