Hybrid Deep Learning Framework for Sleep Quality Prediction: Integrating Metaheuristic Optimization and Statistical Features

Assessing sleep quality is essential to preserving optimum health and well-being, with consequences ranging from preventing chronic diseases to improving cognitive function. This paper introduces a sophisticated hybrid deep learning architecture that far outperforms current techniques for actigraphy data-based sleep quality prediction. Our method uses two metaheuristic optimization approaches (genetic algorithms and particle swarm optimization (PSO)) for feature selection and combines statistical characteristics with complex features retrieved using long short-term memory (LSTM) networks. support vector machines (SVMs) are then used to classify the optimized feature set. Our model outperforms baseline LSTM and other cutting-edge methods when tested on the benchmark MESA Actigraphy dataset. It achieves remarkable accuracy (84.64% for weekly sleep quality and 68.99% for sleep consistency), F1-scores (0.847 and 0.69, respectively), and AUC values (0.909 and 0.839, respectively). Furthermore, we close a significant gap in black-box deep learning techniques by introducing a unique feature significance analysis that gives the model's predictions interpretability. Our results emphasize the potential of hybrid deep learning frameworks for individualized sleep health management and early diagnosis of sleep disorders by demonstrating the efficacy of integrating metaheuristic optimization with multimodal data in sleep quality prediction.