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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.7
- 수록면
- 210 - 221 (12page)
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Objectives: This study presents PD Predict, a machine learning system for Parkinson disease classification using voice as abiomarker. Methods: We first created an original set of recordings from the mPower study, and then extracted several audiofeatures, such as mel-frequency cepstral coefficient (MFCC) components and other classical speech features, using a windowingprocedure. The generated dataset was then divided into training and holdout sets. The training set was used to traintwo 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 implementedin PD Predict and accessed through a prediction endpoint developed using the Django REST Framework. PD Predict is atwo-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 theextracted 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 thenested subject-wise cross-validation approach. Furthermore, they generalized well to unseen data and they did not overfit thetraining set. Conclusions: The architecture of PD Predict is clear, and the performance of the implemented machine learningpipelines is promising and confirms the usability of smartphone microphones for capturing digital biomarkers of disease.
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