Sleep Disorder Analysis: Unveiling the Interplay Between Lifestyle Health and Sleep Quality

This study embarks on a comprehensive investigation into how various lifestyle and health factors impact sleep quality and disorders. It specifically focuses on elements such as sleep duration, physical activity, stress levels, Body Mass Index (BMI), and other related factors. By utilizing advanced machine learning models, including Random Forest and Support Vector Machine, this research dives deep into a dataset enriched with demographic, occupational, and health indicators. The objective is to meticulously unravel the key factors that significantly influence sleep patterns and disorders.The methodology adopted in this research is multidimensional, involving rigorous data preprocessing, in-depth exploratory analysis, innovative feature engineering, and meticulous model evaluation. This approach allows for a thorough understanding of the complex relationships between various lifestyle and health factors and their impact on sleep. The research findings offer valuable insights into the intricate dynamics between stress levels, physical activity, and sleep disorders. These insights are of paramount importance for healthcare professionals seeking actionable information to address sleep-related health issues.Moreover, the study acknowledges the limitations in the scope of the data and the complexity of the models used, suggesting potential areas for future research. This research contributes significantly to the field of sleep health by employing machine learning techniques to analyze and interpret complex datasets, ultimately aiding in the development of more effective strategies for improving sleep quality and managing sleep disorders.