Authors - Ritesh Jawarkar, Reena Satpute, Sudhir Agarmore Abstract - Because sleep problems can influence the health of a person and his/her quality of life, such diagnosis and treatment relies on specific classification. Even though single deep learning and machine learning models have shown their potential, they are limited by overfitting and bias in the model. In order to solve these issues, the current research proposes the expansion of the ensemble learning-based sleep disorder classification through the inclusion of machine learning model predictions. A voting classifier enhances the optimization base classifier outputs in terms of robustness and classification accuracy. According to Sleep Health and Lifestyle Dataset, the ensemble method has 97.3 percent accuracy with individual models. The interface is designed as a Flask-based web interface that allows user authentication to increase user interaction and usage of the system on a real-time basis. Suggested extension ensures the reliable, accurate and easy-to-use automated sleep problem diagnosis.
Open Zoom