Multi-Class Classification in Parkinson's Disease by Leveraging Internal Topological Structure of the Data and of the Label Space

Alex Frid, Larry M. Manevitz, Ohad Mosafi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In recent work, attacks on automated classification of Parkinson's disease have encountered difficulties, especially for cross-individual generalization. This is crucial since (i) Classifying the degree of Parkinson's disease is an important clinical necessity. (ii) The lack of such an automated system leaves current clinical methodology to use manual and subjective classification by a trained clinician. In earlier work, two of the authors of this paper have shown that, directly from the speech signal, reliable classification as to the presence of the disease can be produced using a machine learning approach. However, this approach was unable to reliably classify the severity degree of the disease. In other work, a deep (convolutional) neural network was tried on the same data set (albeit without feature extraction), which again did not succeed on the multi-label case.In this work, we applied a data science approach to solve this problem by analysing the topological structure of the label space and the internal topological structure of the data. Specifically we explored using (i) the linearity of the label-space to reduce the inherent noise in multi-class classifiers and (ii) to break the data into separate topological clusters (by using a version of unsupervised topological learning) and then applying separate classification parametrizations for each cluster.While our interest was mainly directed to the Parkinson's classification problem, the methods seem relatively generic and should be applicable to many data sets. (As an example, we also applied this directly to a well-known baseline data set - wine classification and obtained state of the art results).On the Parkinson classification task, these methods obtained, on a 7 degree classification scale, results which are comparable to the best accuracy on simple two class classification.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
StatePublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
Country/TerritoryHungary
CityBudapest
Period14/07/1919/07/19

Bibliographical note

Funding Information:
This work was partially supported by a grant for computational equipment by the Caesarea Rothschild Institute and by a Hardware Grant by NVIDIA Corporation to the Neurocomputation Laboratory.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Kohonen
  • Multi-Class Classification
  • Parkinson's disease
  • SOM
  • Speech Signal Analysis
  • Topological Clustering

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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