<![CDATA[Sleep disorders are one of the major public health issues with broad implications for quality of life, productivity, and chronic disease risks. This study aims to predict risk factors of sleep disorders using a machine learning approach with survey data from the National Sleep Foundation. The research process involved data Cleaning, transformation, normalization, and splitting into training (80%) and testing (20%) sets. Two algorithms were applied, Logistic Regression and Gradient Boosting, and their performance was evaluated using Accuracy, Precision, Recall, and F1-score metrics. SHAP analysis was also employed to assess variable contributions to model predictions. The results indicate that Gradient Boosting outperformed Logistic Regression, achieving perfect Accuracy and F1-score (1.00), while Logistic Regression only reached 0.70. SHAP analysis revealed that sleep duration and quality are the most influential factors, followed by caffeine consumption and age. Therefore, Gradient Boosting not only provides accurate classification but also comprehensive insights into key determinants of sleep disorders, serving as a foundation for more effective health interventions.]]>
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