Data Analytics and Data Science in Diagnosing and Managing Sleep Disorders Using Wearable Devices

Sleep disorders, such as insomnia and sleep apnea, negatively affect health and quality of life. The development of wearable technology has improved sleep monitoring by enabling continuous, real-time data collection. This review examines how data analytics and data science transform raw sleep data into valuable insights. It discusses various methodologies, applications, challenges, and future possibilities in sleep disorder management. By analyzing recent research, this paper highlights how data-driven techniques enhance diagnostic accuracy and enable personalized treatment strategies. The integration of machine learning and predictive analytics helps identify sleep patterns, detect abnormalities, and improve intervention outcomes. Advancements in this field offer a promising future for more efficient and accessible sleep disorder management, leading to better sleep health and overall well-being.