AI-ENABLED APPROACH FOR PREDICTION OF OBSTRUCTIVE SLEEP APNEA FROM 3D FACIAL DEPTH IMAGES
Abstract
The Sleep Health and Lifestyle Dataset is a comprehensive collection of sleep and lifestyle-related variables for 400 individuals, providing valuable insights into sleep patterns, daily habits, and potential sleep disorders. By analyzing physical activity levels, stress, and BMI categories, healthcare professionals can design personalized lifestyle interventions to improve overall health and well-being. The presence or absence of sleep disorders, such as Insomnia and Sleep Apnea, allows for the identification of individuals at risk and informs targeted diagnostic and therapeutic strategies.
Previous research on sleep health and lifestyle factors often relies on self-reported data, which may introduce biases and inaccuracies. Subjective sleep quality assessments may not capture objective sleep measurements accurately. Additionally, existing studies lack comprehensive datasets, hindering the ability to analyze multiple factors simultaneously.
To address the limitations of existing research, this work proposes a multi-faceted analysis approach. Develop a machine learning model to predict the presence of sleep disorders based on a combination of sleep-related and lifestyle variables. The model can assist in early detection and intervention. The dataset allows for the investigation of sleep duration, quality, and factors influencing sleep patterns, enabling researchers to identify trends and correlations related to sleep health.
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