Data
parkinsons

parkinsons

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael Gomes Mantovani
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Author: Source: UCI Please cite: 'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) * Abstract: Oxford Parkinson's Disease Detection Dataset * Source: The dataset was created by Max Little of the University of Oxford, in collaboration with the National Centre for Voice and Speech, Denver, Colorado, who recorded the speech signals. The original study published the feature extraction methods for general voice disorders. * Data Set Information: This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD. Further details are contained in the following reference -- if you use this dataset, please cite: Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2008), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', IEEE Transactions on Biomedical Engineering (to appear). * Attribute Information: Matrix column entries (attributes): name - ASCII subject name and recording number MDVP:Fo(Hz) - Average vocal fundamental frequency MDVP:Fhi(Hz) - Maximum vocal fundamental frequency MDVP:Flo(Hz) - Minimum vocal fundamental frequency MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several measures of variation in fundamental frequency MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA - Several measures of variation in amplitude NHR,HNR - Two measures of ratio of noise to tonal components in the voice status - Health status of the subject (one) - Parkinson's, (zero) - healthy RPDE,D2 - Two nonlinear dynamical complexity measures DFA - Signal fractal scaling exponent spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation

23 features

Class (target)nominal2 unique values
0 missing
V1numeric195 unique values
0 missing
V2numeric195 unique values
0 missing
V3numeric195 unique values
0 missing
V4numeric173 unique values
0 missing
V5numeric19 unique values
0 missing
V6numeric155 unique values
0 missing
V7numeric165 unique values
0 missing
V8numeric180 unique values
0 missing
V9numeric188 unique values
0 missing
V10numeric149 unique values
0 missing
V11numeric184 unique values
0 missing
V12numeric189 unique values
0 missing
V13numeric189 unique values
0 missing
V14numeric189 unique values
0 missing
V15numeric185 unique values
0 missing
V16numeric195 unique values
0 missing
V17numeric195 unique values
0 missing
V18numeric195 unique values
0 missing
V19numeric195 unique values
0 missing
V20numeric194 unique values
0 missing
V21numeric195 unique values
0 missing
V22numeric195 unique values
0 missing

107 properties

195
Number of instances (rows) of the dataset.
23
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
22
Number of numeric attributes.
1
Number of nominal attributes.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.22
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
21.99
Maximum kurtosis among attributes of the numeric type.
-5.68
Minimum of means among attributes of the numeric type.
1.62
Second quartile (Median) of skewness among attributes of the numeric type.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
197.1
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
4.35
Percentage of binary attributes.
0.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.12
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-0.51
Minimum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
11.36
Third quartile of kurtosis among attributes of the numeric type.
0.95
Average class difference between consecutive instances.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.22
Maximum skewness among attributes of the numeric type.
24.62
Percentage of instances belonging to the least frequent class.
95.65
Percentage of numeric attributes.
1.13
Third quartile of means among attributes of the numeric type.
0.75
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
91.49
Maximum standard deviation of attributes of the numeric type.
48
Number of instances belonging to the least frequent class.
4.35
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.18
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2.76
Third quartile of skewness among attributes of the numeric type.
0.48
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
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
5.52
Mean kurtosis among attributes of the numeric type.
0.29
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.15
First quartile of kurtosis among attributes of the numeric type.
0.56
Third quartile of standard deviation of attributes of the numeric type.
0.75
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
22.2
Mean of means among attributes of the numeric type.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.01
First quartile of means among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.18
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.48
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
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.43
First quartile of skewness among attributes of the numeric type.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.75
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.01
First quartile of standard deviation of attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.18
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.66
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.48
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
0.08
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
75.38
Percentage of instances belonging to the most frequent class.
8.32
Mean standard deviation of attributes of the numeric type.
2.98
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.81
Entropy of the target attribute values.
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
147
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.04
Second quartile (Median) of means among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.92
Minimum kurtosis among attributes of the numeric type.

13 tasks

148 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: V1
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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