Improving Sleep Health Through Data-Driven Machine Learning Insights

Sleep is a critical component of overall health and well-being, yet sleep disorders and poor sleep quality affect millions worldwide. Traditional approaches to sleep assessment, often reliant on self-reports and polysomnography, can be costly and impractical for long-term monitoring. Our approach includes Machine learning algorithms, including random forests, gradient boosting, and data visualization techniques, are employed to predict sleep stages and identify factors influencing sleep quality. Experimental results demonstrate the efficacy of the proposed models, achieving high accuracy in sleep stage classification and actionable insights for sleep improvement. This work underscores the potential of machine learning to revolutionize sleep health management by providing scalable, data-driven, and personalized solutions.