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parkinson-speech-uci

parkinson-speech-uci

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Source: C. Okan Sakar a, Gorkem Serbes b, Aysegul Gunduz c, Hunkar C. Tunc a, Hatice Nizam d, Betul Erdogdu Sakar e, Melih Tutuncu c, Tarkan Aydin a, M. Erdem Isenkul d, Hulya Apaydin c a Department of Computer Engineering, Bahcesehir University, Istanbul, 34353, Turkey b Department of Biomedical Engineering, Yildiz Technical University, Istanbul, 34220, Turkey c Department of Neurology, CerrahpaAYa Faculty of Medicine, Istanbul University, Istanbul 34098, Turkey d Department of Computer Engineering, Istanbul University, Istanbul, 34320, Turkey e Department of Software Engineering, Bahcesehir University, Istanbul, 34353, Turkey e-mails: {okan.sakar '@' eng.bau.edu.tr; gserbes '@' yildiz.edu.tr; draysegulgunduz '@' yahoo.com; hunkarcan.tunc '@' stu.bahcesehir.edu.tr; haticenizam92 '@' gmail.com; betul.erdogdu '@' eng.bau.edu.tr; tutuncumelih '@' yahoo.com; tarkan.aydin '@' eng.bau.edu.tr; eisenkul '@' istanbul.edu.tr; hulyapay '@' istanbul.edu.tr} Data Set Information: The data used in this study were gathered from 188 patients with PD (107 men and 81 women) with ages ranging from 33 to 87 (65.1A+-10.9) at the Department of Neurology in CerrahpaAYa Faculty of Medicine, Istanbul University. The control group consists of 64 healthy individuals (23 men and 41 women) with ages varying between 41 and 82 (61.1A+-8.9). During the data collection process, the microphone is set to 44.1 KHz and following the physicianaEUR(tm)s examination, the sustained phonation of the vowel /a/ was collected from each subject with three repetitions. Attribute Information: Various speech signal processing algorithms including Time Frequency Features, Mel Frequency Cepstral Coefficients (MFCCs), Wavelet Transform based Features, Vocal Fold Features and TWQT features have been applied to the speech recordings of Parkinson's Disease (PD) patients to extract clinically useful information for PD assessment.

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tqwt_medianValue_dec_7numeric663 unique values
0 missing
det_LT_entropy_log_2_coefnumeric755 unique values
0 missing
tqwt_medianValue_dec_8numeric615 unique values
0 missing
det_LT_entropy_log_3_coefnumeric755 unique values
0 missing
tqwt_medianValue_dec_9numeric669 unique values
0 missing
det_LT_entropy_log_4_coefnumeric755 unique values
0 missing
tqwt_medianValue_dec_10numeric672 unique values
0 missing

62 properties

756
Number of instances (rows) of the dataset.
754
Number of attributes (columns) of the dataset.
0
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.
754
Number of numeric attributes.
0
Number of nominal attributes.
15054137.64
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.24
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
-2
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.18
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-9029632763.22
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
717.5
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
79.75
Third quartile of kurtosis among attributes of the numeric type.
12099556.1
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of numeric attributes.
1.24
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-25.67
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
4.85
Third quartile of skewness among attributes of the numeric type.
26.48
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
2.19
First quartile of kurtosis among attributes of the numeric type.
36.18
Third quartile of standard deviation of attributes of the numeric type.
5703424049.27
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
-0
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
63.68
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-23637050.41
Mean of means among attributes of the numeric type.
-0.23
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.01
First quartile of standard deviation of attributes of the numeric type.
0.87
Average class difference between consecutive instances.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
Average number of distinct values among the attributes of the nominal type.
14.11
Second quartile (Median) of kurtosis among attributes of the numeric type.
1
Number of attributes divided by the number of instances.
2.27
Mean skewness among attributes of the numeric type.
0.02
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.

12 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class
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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