Data
parkinsons

parkinsons

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
0 likes downloaded by 13 people , 26 total downloads 0 issues 0 downvotes
  • mf_less_than_80 study_123 study_127 study_50 study_52 study_7 study_88
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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

19 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.
0.95
Average class difference between consecutive instances.
0
Percentage of missing values.
0.12
Number of attributes divided by the number of instances.
95.65
Percentage of numeric attributes.
75.38
Percentage of instances belonging to the most frequent class.
4.35
Percentage of nominal attributes.
147
Number of instances belonging to the most frequent class.
24.62
Percentage of instances belonging to the least frequent class.
48
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
4.35
Percentage of binary attributes.
0
Percentage of instances having missing values.

5 tasks

161 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
47 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
Define a new task