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lsvt

lsvt

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Author: Athanasios Tsanas Source: UCI Please cite: A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: Objective automatic assessment of rehabilitative speech treatment in Parkinsons disease, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 Dataset title laLSVT Voice Rehabilitation Data Set Source: The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com) of the University of Oxford. Abstract: 126 samples from 14 participants, 309 features. Aim: assess whether voice rehabilitation treatment lead to phonations considered 'acceptable' or 'unacceptable' (binary class classification problem). Data Set Information: The original paper demonstrated that it is possible to correctly replicate the experts' binary assessment with approximately 90% accuracy using both 10-fold cross-validation and leave-one-subject-out validation. We experimented with both random forests and support vector machines, using standard approaches for optimizing the SVM's hyperparameters. It will be interesting if researchers can improve on this finding using advanced machine learning tools. Details for the dataset can be found on the following paper. A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: “Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 A freely available preprint is availabe from the first author's website. Attribute Information: Each attribute (feature) corresponds to the application of a speech signal processing algorithm which aims to characterise objectively the signal. These algorithms include standard perturbation analysis methods, wavelet-based features, fundamental frequency-based features, and tools used to mine nonlinear time-series. Because of the extensive number of attributes we refer the interested readers to the relevant papers for further details. Relevant Papers: The dataset was introduced in: A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: “Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 Further details about the speech signal processing algorithms can be found in: A. Tsanas, Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning, D.Phil. (Ph.D.) thesis, University of Oxford, UK, 2012 A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity”, Journal of the Royal Society Interface, Vol. 8, pp. 842-855, 2011 A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “New nonlinear markers and insights into speech signal degradation for effective tracking of Parkinson’s disease symptom severity”, International Symposium on Nonlinear Theory and its Applications (NOLTA), pp. 457-460, Krakow, Poland, 5-8 September 2010 Preprints are available on the first author's website.

311 features

Class (target)nominal2 unique values
0 missing
V1numeric126 unique values
0 missing
V257numeric125 unique values
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V2numeric126 unique values
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V258numeric126 unique values
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V213numeric126 unique values
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V214numeric126 unique values
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V215numeric126 unique values
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0 missing
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V233numeric126 unique values
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V235numeric126 unique values
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V236numeric126 unique values
0 missing
V237numeric126 unique values
0 missing
V238numeric126 unique values
0 missing
V239numeric126 unique values
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V241numeric126 unique values
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V242numeric126 unique values
0 missing
V243numeric126 unique values
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V244numeric126 unique values
0 missing
V245numeric126 unique values
0 missing
V246numeric126 unique values
0 missing
V247numeric126 unique values
0 missing
V248numeric126 unique values
0 missing
V249numeric126 unique values
0 missing
V250numeric126 unique values
0 missing
V251numeric23 unique values
0 missing
V252numeric43 unique values
0 missing
V253numeric62 unique values
0 missing
V254numeric108 unique values
0 missing
V255numeric113 unique values
0 missing
V256numeric123 unique values
0 missing

62 properties

126
Number of instances (rows) of the dataset.
311
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.
310
Number of numeric attributes.
1
Number of nominal attributes.
0.32
Percentage of binary attributes.
3.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.59
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
122.84
Maximum kurtosis among attributes of the numeric type.
-15755088925.87
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
50.01
Third quartile of kurtosis among attributes of the numeric type.
18570761.39
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.68
Percentage of numeric attributes.
50.18
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.
0.32
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.
-11.03
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
5.73
Third quartile of skewness among attributes of the numeric type.
10.84
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0.9
First quartile of kurtosis among attributes of the numeric type.
440.86
Third quartile of standard deviation of attributes of the numeric type.
14132861569.78
Maximum standard deviation of attributes of the numeric type.
33.33
Percentage of instances belonging to the least frequent class.
-0
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.
42
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
27.37
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
-0.1
First quartile of skewness among attributes of the numeric type.
-137218838.85
Mean of means among attributes of the numeric type.
0.03
First quartile of standard deviation of attributes of the numeric type.
0.34
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.92
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.
7.47
Second quartile (Median) of kurtosis among attributes of the numeric type.
2.47
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.07
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.
2.04
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
66.67
Percentage of instances belonging to the most frequent class.
121542465.02
Mean standard deviation of attributes of the numeric type.
1.23
Second quartile (Median) of skewness among attributes of the numeric type.
84
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

4 tasks

131 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
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