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

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.