Parkinson_Dataset from openml contains voice measurement data from individuals, some with Parkinson's disease. The dataset includes columns such as 'MDVP:Fo(Hz)', 'MDVP:Jitter(%)', 'NHR', 'HNR', and a binary 'Status' indicator for disease presence. It was contributed by Jaina under a CC0-1.0 license.
Use Cases
- Train binary classification models to detect Parkinson's disease based on voice frequency and amplitude variation metrics.
- Conduct biomedical research on how Parkinson's disease impacts voice characteristics using nonlinear dynamical measurements like 'RPDE' and 'DFA'.
- Develop diagnostic tools for tracking disease progression through non-invasive voice signal analysis.
Strengths
- Includes a binary 'Status' column explicitly indicating the presence or absence of Parkinson's disease.
- Contains a diverse range of voice signal attributes, including measures of frequency variation, amplitude variation, and noise components.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
Provenance
- Source
- Jaina via openml
- Collection Method
- Collection of voice measurement data from individuals.
- Time Range
- null
- Freshness
- Last update date is unknown; freshness unverified.
- Geography
- null