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.
A tibble with 1,013 observations and 3 variables
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:DDPSeveral measures of variation in fundamental frequency
MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDASeveral measures of variation in amplitude
NHR,HNRTwo measures of ratio of noise to tonal components in the voice
statusHealth status of the subject (one) - Parkinson's, (zero) - healthy
RPDE,D2Two nonlinear dynamical complexity measures
DFASignal fractal scaling exponent
spread1,spread2,PPEThree nonlinear measures of fundamental frequency variation
The data is available at The UCI Machine Learning Repository 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.
The data are originally analysed in: 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.