Literature survey on machine learning approaches for sleep disorder diagnosis

Accurate diagnosis of sleep disorders, such as insomnia and sleep apnea, is crucial for improving health and well-being. Traditional diagnostic methods rely on expert analysis, which can be time-consuming and prone to errors. This Project aims to optimize machine learning approaches to enhance sleep disorder classification using the Sleep Health and Lifestyle Dataset. Various preprocessing techniques, including feature selection and data balancing, will be used to improve model performance. Multiple classifiers will be evaluated, with ensemble methods such as Gradient Boosting and Voting achieving the highest accuracy. The Project aims for optimization in machine learning techniques in predicting sleep disorders, offering a scalable and efficient solution for early diagnosis and personalized health recommendations.