Sleep Disorder Classification and Cause Analysis using Deep Learning

Abstract: Sleep disorders common in aging populations impact not only physical but also mental health. Early identification and knowledge about the root factors play an important role in enhancing quality of life as well as avoiding lasting health complications. In this study introduces a hybrid deep learning approach to not just classify sleep disorders like insomnia and sleep apnea but also determine the responsible factors. A hybrid Convolutional Neural Network (CNN) with Gated Recurrent Units (GRU) model with an attention mechanism is used to extract spatial as well as temporal features from structured health records. SMOTE (Synthetic Minority Over-sampling Technique) is applied in the model to treat class imbalance, allowing for better classification. LIME (Local Interpretable Model-Agnostic Explanations) is also applied to offer insights into the most influencing factors behind the predicted sleep disorder for every sample, allowing for individualized suggestions. Upon prolonged training and through these methodologies combined, the model scores over 90% accuracy, providing an efficient tool to diagnose sleep disorders with high accuracy. This also opens doors to discover the interrelationship among different physiologic and lifestyle factors, thus leading to tailored treatment plans for individuals facing sleep disorders.