Parkinson's Disease Prediction
Abstract: Oxford Parkinson's Disease Telemonitoring Dataset
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The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com) and Max Little (littlem '@' physics.ox.ac.uk) of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation who developed the telemonitoring device to record the speech signals. The original study used a range of linear and nonlinear regression methods to predict the clinician's Parkinson's disease symptom score on the UPDRS scale.
Little MA, McSharry PE, Hunter EJ, Ramig LO (2009),
'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease',
IEEE Transactions on Biomedical Engineering, 56(4):1015-1022
Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM.
'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection',
BioMedical Engineering OnLine 2007, 6:23 (26 June 2007)
The evaluation of this dataset is done using Area Under the ROC curve (AUC).
Interpreting the AUROC
Computing the AUROC
Source : http://stats.stackexchange.com/questions/132777/what-does-auc-stand-for-and-what-is-it
- Use of external data is not permitted. This includes use of pre-trained models.
- Hand-labeling is allowed on the training dataset only. Hand-labeling is not permitted on test data and will be grounds for disqualification.
If you use this dataset, please cite the following paper: