Data
parkinsons

parkinsons

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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  • mf_less_than_80 study_123 study_127 study_50 study_52 study_7 study_88
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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

62 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.
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.
91.49
Maximum standard deviation of attributes of the numeric type.
24.62
Percentage of instances belonging to the least frequent class.
0.01
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
48
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
5.52
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
0.43
First quartile of skewness among attributes of the numeric type.
22.2
Mean of means among attributes of the numeric type.
0.01
First quartile of standard deviation of attributes of the numeric type.
0.95
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.81
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2.98
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.12
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.04
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.66
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
75.38
Percentage of instances belonging to the most frequent class.
8.32
Mean standard deviation of attributes of the numeric type.
1.62
Second quartile (Median) of skewness among attributes of the numeric type.
147
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.92
Minimum kurtosis among attributes of the numeric type.
4.35
Percentage of binary attributes.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
21.99
Maximum kurtosis among attributes of the numeric type.
-5.68
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
11.36
Third quartile of kurtosis among attributes of the numeric type.
197.1
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
95.65
Percentage of numeric attributes.
1.13
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
4.35
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.51
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.76
Third quartile of skewness among attributes of the numeric type.
4.22
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.

5 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
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