<![CDATA[Sleep disorders are a medical condition with a continuously increasing prevalence globally, including in Indonesia, and have a serious impact on an individual’s quality of life, cognitive function, and cardiovascular health. This study aims to classify types of sleep disorders using the Naïve Bayes algorithm as a probabilistic approach in health data mining. The dataset used is the Sleep Health and Lifestyle Dataset, consisting of 400 data records with 13 attributes, covering demographic factors, lifestyle, and physiological parameters. The research stages included data preprocessing, blood pressure feature engineering, and splitting the data into training and test sets with an 80:20 ratio. Model evaluation was performed using a confusion matrix and accuracy, precision, recall, and F1-score metrics. The results showed that the Naïve Bayes model achieved an accuracy of 71.60% on the 81-sample test set. The best performance was observed in the "None" class with an F1-score of 0.83, while the "Insomnia" and "Sleep Apnea" classes could not be successfully identified by the model due to the dominance of the majority class (class imbalance). An example prediction for a 47-year-old female subject with a sleep quality of 7/10, a stress level of 4/10, and a Normal BMI resulted in a classification of "No Sleep Disorder" with a probability of 72.08%. These findings indicate that addressing class imbalance is necessary to improve the classification performance of the Naïve Bayes model on the sleep disorder dataset.]]>
