User Profiles of Sleep Disorder Sufferers: Towards Explainable Clustering and Differential Variable Analysis
This work addresses the complex diagnosis of sleep disorders for patients and clinicians, but it appears incremental as it applies existing explainable AI techniques to medical data.
The study tackled the problem of diagnosing sleep disorders by proposing a clustering-based method to group patients into profiles and identify key factors using explainable AI, with an experiment on real data demonstrating its effectiveness.
Sleep disorders have a major impact on patients' health and quality of life, but their diagnosis remains complex due to the diversity of symptoms. Today, technological advances, combined with medical data analysis, are opening new perspectives for a better understanding of these disorders. In particular, explainable artificial intelligence (XAI) aims to make AI model decisions understandable and interpretable for users. In this study, we propose a clustering-based method to group patients according to different sleep disorder profiles. By integrating an explainable approach, we identify the key factors influencing these pathologies. An experiment on anonymized real data illustrates the effectiveness and relevance of our approach.