Hybrid Mamba and Attention-Enhanced Bi-LSTM for Obesity Classification and Key Determinant Identification

Obesity is a major public health challenge linked to increased risks of chronic diseases. Effective prevention and intervention strategies require accurate classification and identification of key determinants. This study aims to develop a robust deep learning framework to enhance the accuracy and interpretability of obesity classification using comprehensive datasets, and to compare its performance with both traditional and state-of-the-art deep learning models. We propose a hybrid deep learning framework that combines an improved Mamba model with an attention-enhanced bidirectional LSTM (ABi-LSTM). The framework utilizes the Obesity and CDC datasets. A feature tokenizer is integrated into the Mamba model to improve scalability and representation learning. Channel-independent processing is employed to prevent overfitting through independent feature analysis. The ABi-LSTM component is used to capture complex temporal dependencies in the data, thereby enhancing classification performance. The proposed framework achieved an accuracy of 93.42%, surpassing existing methods such as ID3 (91.87%), J48 (89.98%), Naïve Bayes (90.31%), Bayesian Network (89.23%), as well as deep learning-based approaches such as VAE (92.12%) and LightCNN (92.50%). Additionally, the model improved sensitivity to 91.11% and specificity to 92.34%. The hybrid model demonstrates superior performance in obesity classification and determinant identification compared to both traditional and advanced deep learning methods. These results underscore the potential of deep learning in enabling data-driven personalized healthcare and targeted obesity interventions.