<![CDATA[Sleep disorders are a health problem that significantly impacts quality of life and potentially increases the risk of various chronic diseases. Conventional sleep disorder diagnosis generally requires expensive and complex examinations, so an alternative, non-invasive data-driven approach is needed. This study proposes a sleep disorder classification approach based on tabular medical data using a Hybrid Two-Stage architecture. The proposed approach integrates the CatBoost algorithm as a binary screening stage to distinguish healthy individuals from individuals with sleep disorders, and a Multilayer Perceptron (MLP) as a latent feature extractor, combined with CatBoost to classify sleep disorder subtypes, namely insomnia and sleep apnea. The datasets used were obtained from two public data sources and evaluated using a stratified k-fold cross-validation scheme. Class imbalance was addressed using the SMOTE-ENN technique, while hyperparameter optimization was applied as part of the model training pipeline. Performance evaluation was conducted using accuracy, Macro-F1, and Matthews Correlation Coefficient (MCC) metrics. Experimental results show that the Hybrid Two-Stage architecture achieves an accuracy of 94.1%, a Macro-F1 of 0.90, and an MCC of 0.88, and exhibits stable performance across a wide range of fold variations. These results demonstrate that the hybrid two-stage approach is effective in improving the performance of sleep disorder classification based on medical tabular data. The main contribution of this study is the development of a two-stage hybrid classification framework that explicitly separates healthy-disorder screening and sleep disorder subtype classification, while integrating SMOTE-ENN, Optuna-based hyperparameter optimization, and MLP-derived latent feature representation to improve classification stability on imbalanced medical tabular data.]]>
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