SLEEP DISORDER PREDICTION

Abstract: This paper presents a machine learning–based Sleep Disorder Prediction System designed to assist in the early identification and awareness of common sleep-related disorders. The system analyzes user-provided health and lifestyle parameters such as sleep duration, body mass index (BMI), stress level, physical activity, heart rate, and blood pressure to predict sleep conditions including Healthy, Insomnia, and Sleep Apnea. By integrating a trained classification model with a Django-based web application, the system provides real-time predictions along with confidence scores to help users better understand their sleep health status. The platform also includes features such as user authentication, prediction history tracking, doctor appointment booking, and contact support, making it practical for real-world usage. This approach demonstrates how data-driven machine learning techniques can offer an accessible, cost-effective, and user-friendly solution for preliminary sleep disorder assessment and promote proactive sleep health management.

Keywords: Sleep Disorder Prediction, Machine Learning, Insomnia, Sleep Apnea, Healthcare Analytics, Web Application.