30 disease categories mapped to synthetic patient symptom profiles for classification tasks. The data provides a structured mapping between clinical signs and diagnostic labels to facilitate automated medical categorization.
Use Cases
- Train a classification model to predict the disease label based on symptom features
- Evaluate model performance across 30 distinct disease categories
- Identify the most predictive symptoms for specific diseases using feature importance techniques
Strengths
- 30 unique disease labels for multi-class classification
- Synthetic mapping of patient symptoms to diagnostic outcomes
- Structured format optimized for classification pipelines