Parkinsons
Donated on 6/25/2008
Oxford Parkinson's Disease Detection Dataset
Dataset Characteristics
Multivariate
Subject Area
Health and Medicine
Associated Tasks
Classification
Feature Type
Real
# Instances
197
# Features
22
Dataset Information
Additional 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. The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column.For further information or to pass on comments, please contact Max Little (littlem '@' robots.ox.ac.uk). 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).
Has Missing Values?
No
Introductory Paper
By Max A. Little, P. McSharry, S. Roberts, D. Costello, I. Moroz. 2007
Published in BioMedical Engineering OnLine
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| name | ID | Categorical | no | ||
| MDVP:Fo | Feature | Continuous | Hz | no | |
| MDVP:Fhi | Feature | Continuous | Hz | no | |
| MDVP:Flo | Feature | Continuous | Hz | no | |
| MDVP:Jitter | Feature | Continuous | % | no | |
| MDVP:Jitter | Feature | Continuous | Abs | no | |
| MDVP:RAP | Feature | Continuous | no | ||
| MDVP:PPQ | Feature | Continuous | no | ||
| Jitter:DDP | Feature | Continuous | no | ||
| MDVP:Shimmer | Feature | Continuous | no |
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Additional Variable 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
Dataset Files
| File | Size |
|---|---|
| telemonitoring/parkinsons_updrs.data | 889.9 KB |
| parkinsons.data | 39.7 KB |
| telemonitoring/parkinsons_updrs.names | 4.3 KB |
| parkinsons.names | 3 KB |
Papers Citing this Dataset
Sort by Year, desc
By Aaron Klein, Frank Hutter. 2019
Published in ArXiv.
By Matt Barnes, Artur Dubrawski. 2018
Published in Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, Volume 89.
By Alexander New, Curt Breneman, Kristin Bennett. 2018
Published in In 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018.
By Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria, Atul Saroop. 2017
Published in Machine Learning.
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset parkinsons = fetch_ucirepo(id=174) # data (as pandas dataframes) X = parkinsons.data.features y = parkinsons.data.targets # metadata print(parkinsons.metadata) # variable information print(parkinsons.variables)
Little, M. (2007). Parkinsons [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C59C74.
Creators
Max Little
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.