Screening data for autism spectrum disorder in adolescents. The dataset contains behavioral assessment information, sourced from the UCI Machine Learning Repository. Specific temporal coverage and record count are not provided.
Use Cases
- Predict ASD classification from behavioral assessment scores using logistic regression.
- Analyze feature importance of screening questionnaire items for adolescent diagnosis.
- Identify behavioral patterns correlated with ASD using clustering algorithms on assessment data.
- Develop a binary classifier to distinguish ASD from non-ASD cases based on screening responses.
Strengths
- Data originates from the authoritative UCI Machine Learning Repository.
- Focuses on the specific demographic of adolescents.
Limitations
- Sample size and number of features are unknown, limiting reproducibility assessment.
- Potential for class imbalance or geographic bias is not documented.
Provenance
- Source
- UCI Machine Learning Repository
- Collection Method
- Behavioral assessment screening, likely via questionnaire.
- Time Range
- null
- Freshness
- null
- Geography
- null