Data
parkinsons-telemonitoring

parkinsons-telemonitoring

active ARFF Publicly available Visibility: public Uploaded 16-02-2016 by Hilda Fabiola Bernard
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Author: Athanasios Tsanas (tsanasthanasis '@' gmail.com) and Max Little (littlem '@' physics.ox.ac.uk) Source: UCI Please cite: 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. Data Set Information: This dataset is composed of a range of biomedical voice measurements from 42 people with early-stage Parkinson's disease recruited to a six-month trial of a telemonitoring device for remote symptom progression monitoring. The recordings were automatically captured in the patient's homes. Columns in the table contain subject number, subject age, subject gender, time interval from baseline recruitment date, motor UPDRS, total UPDRS, and 16 biomedical voice measures. Each row corresponds to one of 5,875 voice recording from these individuals. The main aim of the data is to predict the motor and total UPDRS scores ('motor_UPDRS' and 'total_UPDRS') from the 16 voice measures. The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around 200 recordings per patient, the subject number of the patient is identified in the first column. For further information or to pass on comments, please contact Athanasios Tsanas (tsanasthanasis '@' gmail.com) or Max Little (littlem '@' physics.ox.ac.uk). Further details are contained in the following reference -- if you use this dataset, please cite: Athanasios Tsanas, Max A. Little, Patrick E. McSharry, Lorraine O. Ramig (2009), 'Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests', IEEE Transactions on Biomedical Engineering (to appear). Further details about the biomedical voice measures can be found in: Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2009), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', IEEE Transactions on Biomedical Engineering, 56(4):1015-1022 Attribute Information: subject# - Integer that uniquely identifies each subject age - Subject age sex - Subject gender '0' - male, '1' - female test_time - Time since recruitment into the trial. The integer part is the number of days since recruitment. motor_UPDRS - Clinician's motor UPDRS score, linearly interpolated total_UPDRS - Clinician's total UPDRS score, linearly interpolated Jitter(%),Jitter(Abs),Jitter:RAP,Jitter:PPQ5,Jitter:DDP - Several measures of variation in fundamental frequency Shimmer,Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,Shimmer:APQ11,Shimmer:DDA - Several measures of variation in amplitude NHR,HNR - Two measures of ratio of noise to tonal components in the voice RPDE - A nonlinear dynamical complexity measure DFA - Signal fractal scaling exponent PPE - A nonlinear measure of fundamental frequency variation 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) Citation Request: If you use this dataset, please cite the following paper: A Tsanas, MA Little, PE McSharry, LO Ramig (2009) 'Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests', IEEE Transactions on Biomedical Engineering (to appear).

22 features

subject.numeric42 unique values
0 missing
agenumeric23 unique values
0 missing
sexnumeric2 unique values
0 missing
test_timenumeric2442 unique values
0 missing
motor_UPDRSnumeric1080 unique values
0 missing
total_UPDRSnumeric1129 unique values
0 missing
Jitter...numeric1305 unique values
0 missing
Jitter.Abs.numeric4105 unique values
0 missing
Jitter.RAPnumeric853 unique values
0 missing
Jitter.PPQ5numeric840 unique values
0 missing
Jitter.DDPnumeric1703 unique values
0 missing
Shimmernumeric3581 unique values
0 missing
Shimmer.dB.numeric852 unique values
0 missing
Shimmer.APQ3numeric2664 unique values
0 missing
Shimmer.APQ5numeric2850 unique values
0 missing
Shimmer.APQ11numeric3283 unique values
0 missing
Shimmer.DDAnumeric4223 unique values
0 missing
NHRnumeric5532 unique values
0 missing
HNRnumeric4780 unique values
0 missing
RPDEnumeric5430 unique values
0 missing
DFAnumeric5282 unique values
0 missing
PPEnumeric4777 unique values
0 missing
Show all 22 features

107 properties

NumberOfInstances
5875
Number of instances (rows) of the dataset.
NumberOfFeatures
22
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
22
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
92.86
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
3.1
Second quartile (Median) of skewness among attributes of the numeric type.
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0
Number of attributes divided by the number of instances.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
0.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.81
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
7.59
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
27.58
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
Average class difference between consecutive instances.
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
53.45
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
21.35
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
21.46
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
4.39
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
11.52
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
-0.49
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
5.25
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
0.02
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
0
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.08
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
2.67
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.01
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
4.5
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
Entropy of the target attribute values.
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
Number of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
13.91
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-1.39
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.14
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
81.57
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Show all 107 properties

11 tasks

Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
Clustering on parkinsons-telemonitoring
0 runs - estimation_procedure: 50 times Clustering
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