Abstract: Oxford Parkinson's Disease Telemonitoring Dataset |
Data Set Characteristics: | Multivariate | Number of Instances: | 5875 | Area: | Life |
Attribute Characteristics: | Integer, Real | Number of Attributes: | 26 | Date Donated | 2009-10-29 |
Associated Tasks: | Regression | Missing Values? | N/A | Number of Web Hits: | 66515 |
Source:
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.
Relevant Papers:
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).
An example of its application are ROC curves. Here, the true positive rates are plotted against false positive rates. An example is below. The closer AUC for a model comes to 1, the better it is. So models with higher AUCs are preferred over those with lower AUCs.
Please note, there are also other methods than ROC curves but they are also related to the true positive and false positive rates, e. g. precision-recall, F1-Score or Lorenz curves.
AUC is used most of the time to mean AUROC, AUC is ambiguous (could be any curve) while AUROC is not.
The AUROC has several equivalent interpretations:
Assume we have a probabilistic, binary classifier such as logistic regression.
Before presenting the ROC curve (= Receiver Operating Characteristic curve), the concept ofconfusion matrix must be understood. When we make a binary prediction, there can be 4 types of outcomes:
To get the confusion matrix, we go over all the predictions made by the model, and count how many times each of those 4 types of outcomes occur:
In this example of a confusion matrix, among the 50 data points that are classified, 45 are correctly classified and the 5 are misclassified.
Since to compare two different models it is often more convenient to have a single metric rather than several ones, we compute two metrics from the confusion matrix, which we will later combine into one:
To combine the FPR and the TPR into one single metric, we first compute the two former metrics with many different threshold (for example
The following figure shows the AUROC graphically:
In this figure, the blue area corresponds to the Area Under the curve of the Receiver Operating Characteristic (AUROC). The dashed line in the diagonal we present the ROC curve of a random predictor: it has an AUROC of 0.5. The random predictor is commonly used as a baseline to see whether the model is useful.
If you want to get some first-hand experience:
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