Analysis of Sleep Health and Lifestyle Factors: A Machine Learning Approach

Abstract

Sleep plays a key role in overall health, yet many factors in daily life can affect its quality. This study explores how lifestyle choices, stress, and physical activity relate to sleep health. Using a dataset of 400 individuals, we analyze sleep duration, quality, and common sleep disorders such as insomnia and sleep apnea. The data also includes information on age, occupation, BMI, blood pressure, heart rate, and daily steps. This work utilized statistical analysis and machine learning models to identify correlations among the key parameters influencing sleep health. The results highlight connections between stress levels, physical activity, and sleep disorders, offering insights into how lifestyle adjustments may support better sleep.