Multimodal Voice and Demographic Features for Diabetes Prediction, 1600 Samples

1600 voice samples are paired with 304 voice features and 4 demographic variables for diabetes prediction. The dataset is hosted on Kaggle and includes platform tags suggesting a focus on deep learning applications and synthetic data. Its multimodal nature combines audio signal processing with demographic information.

Use Cases

• Train a classifier for diabetes risk based on extracted voice features.
• Explore correlations between demographic variables and vocal biomarkers.
• Develop a multimodal fusion model combining audio and demographic data for health screening.
• Benchmark deep learning architectures on a structured health dataset with audio inputs.

Strengths

• Includes 1600 distinct voice samples, providing a foundation for model training.
• Combines 304 voice features with 4 demographic variables, offering multiple data modalities.
• Platform tags indicate relevance for deep learning and synthetic data experiments.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale modeling.
• Data may reflect geographic or source bias inherent to its collection and platform.

Provenance

Source

Kaggle

Collection Method

Likely contains voice recordings processed to extract features, combined with demographic survey data.

Geography

Platform tags suggest a possible focus on Asia, but specific coverage is unknown.