Machine learning-based multilabel classification of six major sleep disorder categories: A multi-center study

Key Points

• Machine learning algorithms accurately classify six sleep disorder categories, enhancing diagnosis precision.
• A key finding includes a classification accuracy of over 85% based on a diverse dataset across multiple centers.
• The approach utilizes multilabel classification techniques to analyze sleep patterns and related biomarkers.
• This study supports the potential for AI-driven tools in improving outcomes for individuals with sleep disorders.