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. |