Paper Title
Automatic Sleep Disorders Classification Using Deep Learning Based on Sleep Quality Features

Abstract
A growing number of sleep disorder cases concentrate medical attention because these medical issues create profound consequences for both physical wellness and mental function. Fast and precise detection of these conditions leads to more effective treatment plans which enhance overall patient results. A deep learning-based automated classification system for sleep disorders will be developed in this project. The research design involves individual application of Convolutional Neural Networks (CNN)and Long Short-Term Memory (LSTM) networks for examining sleep quality measures including sleep duration along with sleep stages and disruptions. The study uses CNNs to identify spatial data characteristics and trains LSTMs to understand the time- dependent aspects of sleep patterns. Researchers will execute a detailed evaluation comparison between the two models using evaluation metrics accuracy, precision and recall. This research evaluates different methods to determine each approach's effective qualities and limitations for sleep disorder classification. The project aims to develop are liable large-scale detection system which can identify different sleep disorders such as insomnia along with sleep apnea as it functions. Healthcare providers stand to benefit substantially from this project because the new automated tool improves sleep-related diagnostic processes to enable better condition management. Keywords - Bankruptcy Prediction, Feature Extraction, Machine Learning, XG Boost, Light GBM, Deep Neural Networks, Financial Distress, Predictive Analytics, Automated Prediction.