@inproceedings{9a814dd2e47443de881594ce8a0768e2,
title = "Associative memory approach for the diagnosis of parkinson's disease",
abstract = "A method for diagnosing Parkinson's disease is presented. The proposal is based on associative approach, and we used this method for classifying patients with Parkinson's disease and those who are completely healthy. In particular, Alpha-Beta Bidirectional Associative Memory is used together with the modified Johnson-M{\"o}bius codification in order to deal with mixed noise. We use three methods for testing the performance of our method: Leave-One-Out, Hold-Out and K-fold Cross Validation and the average obtained was of 97.17%.",
keywords = "Alpha-Beta BAM, Associative Models, Classification, Codification",
author = "Elena Acevedo and Antonio Acevedo and Federico Felipe",
year = "2011",
doi = "10.1007/978-3-642-21587-2_12",
language = "Ingl{\'e}s",
isbn = "9783642215865",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "103--117",
booktitle = "Pattern Recognition - Third Mexican Conference, MCPR 2011, Proceedings",
note = "3rd Mexican Conference on Pattern Recognition, MCPR 2011 ; Conference date: 29-06-2011 Through 02-07-2011",
}