# User Profiles of Sleep Disorder Sufferers: Towards Explainable Clustering and Differential Variable Analysis

arXiv: https://arxiv.org/abs/2510.15986 · HTML: https://mlsanity.com/paper/2510.15986/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Logic-Based Explainability in Machine Learning](https://mlsanity.com/paper/2211.00541/) (2022)
- [On the Computational Intelligibility of Boolean Classifiers](https://mlsanity.com/paper/2104.06172/) (2021)

## Abstract (arXiv)

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.