Parkinson's Disease Classification
Donated on 11/4/2018
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 (65.1±10.9).
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Integer, Real
# Instances
756
# Features
754
Dataset Information
Additional 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 (65.1±10.9) at the Department of Neurology in Cerrahpaşa 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 (61.1±8.9). During the data collection process, the microphone is set to 44.1 KHz and following the physician’s examination, the sustained phonation of the vowel /a/ was collected from each subject with three repetitions.
Has Missing Values?
No
Variable 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.
Dataset Files
| File | Size |
|---|---|
| pd_speech_features.rar | 2 MB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset parkinson_s_disease_classification = fetch_ucirepo(id=470) # data (as pandas dataframes) X = parkinson_s_disease_classification.data.features y = parkinson_s_disease_classification.data.targets # metadata print(parkinson_s_disease_classification.metadata) # variable information print(parkinson_s_disease_classification.variables)
Sakar, C., Serbes, G., Gunduz, A., Nizam, H., & Sakar, B. (2018). Parkinson's Disease Classification [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5MS4X.
Creators
C. Sakar
Gorkem Serbes
Aysegul Gunduz
Hatice Nizam
Betul Sakar
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.