<![CDATA[Sleep disorders such as insomnia and sleep apnea, collectively referred to as somnipathy, are health conditions that can significantly reduce quality of life and are associated with various chronic diseases. However, conventional diagnostic methods such as polysomnography (PSG) have limitations in terms of cost, time, and accessibility. Therefore, this study proposes a machine learning–based approach to classify sleep disorders using a combination of Random Forest integrated with the SMOTE and the DCOA. The dataset used in this study is the Sleep Health and Lifestyle Dataset, which consists of 374 records with 13 features representing individuals’ physiological conditions and lifestyle factors. The issue of data imbalance is addressed using SMOTE, while hyperparameter optimization is performed using DCOA to enhance model performance. The findings indicate that the proposed model achieves an accuracy of 97.33%, precision of 97.63%, recall of 97.33%, and an F1-score of 97.32%. These results demonstrate a significant improvement compared to previous studies using the same dataset. Therefore, the proposed approach proves to be effective in improving sleep disorder classification performance and has strong potential to be implemented as an optimal and accurate data-driven decision support system for diagnosis. However, considering the relatively small dataset size, there is a potential risk of overfitting, which necessitates careful evaluation to ensure model generalization.]]>
