Automatic Sleep Disorder Classification Using Large Language Model Prompting on Sleep Health and Lifestyle Data

Sleep disorders are a major global health issue, affecting nearly a third of the world’s population. This research explores using the sophisticated language and reasoning abilities of large language models (LLMs) to automatically identify these disorders. We trained LLMs on a dataset of sleep patterns, lifestyle choices, and related health factors, employing three novel prompting approaches to guide their design, training, and evaluation of classifiers. Our results show that a support vector machine classifier, identified through decomposed prompting, achieved an impressive 91.9 percent accuracy (F1-score: 0.919), significantly better than traditional zero-shot and few-shot methods. This work demonstrates a powerful integration of LLM’s understanding and reasoning with automated machine learning, offering a promising new direction for sleep disorder classification in health informatics.