improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches

Sleep disorders, including Sleep Apnea and Insomnia, significantly affect individuals' health and quality of life, necessitating accurate and accessible diagnostic methods. Traditional diagnostic tools, such as Polysomnography (PSG), are expensive, time-consuming, and limited in accessibility, often leading to delayed or missed diagnoses. This project aims to address these limitations by leveraging machine learning algorithms for the classification of sleep disorders using the Sleep Health and Lifestyle Dataset. The existing system utilizes traditional algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and Artificial Neural Network (ANN). However, these approaches face challenges like computational overhead, sensitivity to hyperparameters, and limited interpretability. To overcome these issues, the proposed system implements advanced ensemble learning techniques, including the Stacking Classifier and Voting Classifier, to improve accuracy, robustness, and scalability. The project comprises data preprocessing, feature engineering, and model training using health and lifestyle features such as sleep duration, quality of sleep, physical activity, and stress levels. The system also provides users with an intuitive interface to upload data, view predictions, and analyze results. Additionally, it visualizes the distribution of sleep disorder types to enhance diagnostic understanding. By combining the strengths of ensemble learning methods, this project seeks to deliver a cost-effective, user-friendly, and reliable diagnostic system, improving early detection and management of sleep disorders. This innovation contributes to healthcare accessibility, promoting better health outcomes and quality of life.

Keywords: Sleep Disorders, Sleep Apnea, Insomnia, Machine Learning, Ensemble Learning, Stacking Classifier, Voting Classifier.