Parkinson's data set

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).

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.

Citation:

Little,Max. (2008). Parkinsons. UCI Machine Learning Repository. https://doi.org/10.24432/C59C74.

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 Five measures of variation in Frequency MDVP:Jitter(%) - Percentage of cycle-to-cycle variability of the period duration MDVP:Jitter(Abs) - Absolute value of cycle-to-cycle variability of the period duration MDVP:RAP - Relative measure of the pitch disturbance MDVP:PPQ - Pitch perturbation quotient Jitter:DDP - Average absolute difference of differences between jitter cycles Six measures of variation in amplitude MDVP:Shimmer - Variations in the voice amplitdue MDVP:Shimmer(dB) - Variations in the voice amplitdue in dB Shimmer:APQ3 - Three point amplitude perturbation quotient measured against the average of the three amplitude Shimmer:APQ5 - Five point amplitude perturbation quotient measured against the average of the three amplitude MDVP:APQ - Amplitude perturbation quotient from MDVP Shimmer:DDA - Average absolute difference between the amplitudes of consecutive periods Two measures of ratio of noise to tonal components in the voice NHR - Noise-to-harmonics Ratio and HNR - Harmonics-to-noise Ratio status - Health status of the subject (one) - Parkinson's, (zero) - healthy Two nonlinear dynamical complexity measures RPDE - Recurrence period density entropy D2 - correlation dimension DFA - Signal fractal scaling exponent Three nonlinear measures of fundamental frequency variation spread1 - discrete probability distribution of occurrence of relative semitone variations spread2 - Three nonlinear measures of fundamental frequency variation PPE - Entropy of the discrete probability distribution of occurrence of relative semitone variations

Context

Try finding the reasons for Parkinsons disease and predict who might have it next!

Dataset

This dataset was created by Darshan2318

Released under Apache 2.0

Contents

Dataset

This dataset was created by Deep pratap Singh

Released under Apache 2.0

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The Parkinson’s Progression Markers Initiative (PPMI) dataset originates from an observational clinical and longitudinal study comprising evaluations of people with Parkinson’s disease (PD), those people with high risk, and those who are healthy.

Dataset

This dataset was created by aadarsh kumar shah

Released under Apache 2.0

Contents

K. Scott Mader created the original dataset of 204 hand-drawn images for Parkinson’s disease diagnosis, consisting of two classes: Healthy and Parkinson. The dataset includes spiral and wave drawings. For my thesis, the original 204 images were expanded to 3,264 across the same two classes. This increase was achieved through data augmentation techniques, including rotations of 90°, 180°, and 270°, vertical flipping at 180°, and conversion to color images. The augmented data gives the model more opportunities to generalize, enhancing training and testing processes.

Dataset

This dataset was created by KhumanShubh

Contents

Dataset

This dataset was created by Suman Saha

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by aadarsh kumar shah

Released under Apache 2.0

Contents

Dataset

This dataset was created by Sripad (SRH)

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Eric Greene

Released under Apache 2.0

Contents

3_cls folder has 2 directories: - train - test

Each directory has 3 sub-directories: - CONTROL - AD - PD

Dataset is collected from here

Dataset

This dataset was created by falcon

Contents

Context

More than 10 million people worldwide are living with Parkinson's disease. Improving machine learning model which identifies Parkinson's disease will lead to helping patients with early dialogs and reduction of treatment cost.

Content

Handwriting database consists of 62 PWP(People with Parkinson) and 15 healthy individuals. The data was collected in 2009.

Number of instances: 77, Number of attributes: 7

Acknowledgements

Source: https://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+Tablet

Citation:

1.Isenkul, M.E.; Sakar, B.E.; Kursun, O. . 'Improved spiral test using digitized graphics tablet for monitoring Parkinson's disease.' The 2nd International Conference on e-Health and Telemedicine (ICEHTM-2014), pp. 171-175, 2014.

2.Erdogdu Sakar, B., Isenkul, M., Sakar, C.O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., Kursun, O., 'Collection and Analysis of a Parkinson Speech Dataset with Multiple Types of Sound Recordings', IEEE Journal of Biomedical and Health Informatics, vol. 17(4), pp. 828-834, 2013.

Dataset

This dataset was created by Ankit Gupta

Contents

Content

In this dataset you'll find the conditions and characteristics of several patients diagnosed with Parkinson's Disease.

Acknowledgements

This data comes from https://data.world/uci/parkinsons/workspace/file?filename=parkinsons.names.txt.

Dataset

This dataset was created by SHRUTI KUBDE

Released under Apache 2.0

Contents

Dataset

This dataset was created by Kannan.K.R

Contents

Dataset

This dataset was created by Eric Greene

Released under Apache 2.0

Contents