Healthc Inform Res.  2022 Jul;28(3):210-221. 10.4258/hir.2022.28.3.210.

Machine Learning Smart System for Parkinson Disease Classification Using the Voice as a Biomarker

• Tougui I
• Jilbab A
• Mhamdi JE

• KMID: 2532448
• DOI: http://doi.org/10.4258/hir.2022.28.3.210

Abstract

Objectives
This study presents PD Predict, a machine learning system for Parkinson disease classification using voice as a biomarker.
Methods
We first created an original set of recordings from the mPower study, and then extracted several audio features, such as mel-frequency cepstral coefficient (MFCC) components and other classical speech features, using a windowing procedure. The generated dataset was then divided into training and holdout sets. The training set was used to train two machine learning pipelines, and their performance was estimated using a nested subject-wise cross-validation approach. The holdout set was used to assess the generalizability of the pipelines for unseen data. The final pipelines were implemented in PD Predict and accessed through a prediction endpoint developed using the Django REST Framework. PD Predict is a two-component system: a desktop application that records audio recordings, extracts audio features, and makes predictions; and a server-side web application that implements the machine learning pipelines and processes incoming requests with the extracted audio features to make predictions. Our system is deployed and accessible via the following link: https://pdpredict.herokuapp.com/.
Results
Both machine learning pipelines showed moderate performance, between 65% and 75% using the nested subject-wise cross-validation approach. Furthermore, they generalized well to unseen data and they did not overfit the training set.
Conclusions
The architecture of PD Predict is clear, and the performance of the implemented machine learning pipelines is promising and confirms the usability of smartphone microphones for capturing digital biomarkers of disease.

Keyword

Parkinson Disease; Voice Disorders; Machine Learning; Diagnosis; Computer-Assisted; Medical Informatics Applications

Figure

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  Figure 1 Schematic illustration of the nested cross-validation approach.

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  Figure 2 Various screens in the clientside desktop application.

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  Figure 3 Architecture of PD Predict: (A) client-side application and (B) server-side web application.

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  Figure 4 (A) The gbcpl cross-validation performance using a different subset of features and (B) the final 60 chosen features.

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  Figure 5 (A) Performance of gbcpen in cross-validation using different subsets of features and (B) the final 60 chosen features.

Reference

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