In-Depth Sleep Disorder Assessment using REMcraft Navigator IoT and Random Forest Algorithms

To accurately diagnose and treat sleep problems, which are major challenges to public health, more sophisticated evaluation methods are needed. Using Rapid Eye Movement (REM) craft Navigator’s Internet of Things (IoT) capabilities in conjunction with Random Forest algorithms, this research presents a novel method for comprehensive assessments of sleep disorders. The REMcraft Navigator IoT system incorporates a wide range of physiological and environmental factors into its comprehensive real-time data collection on sleep patterns. The research uses Random Forest algorithms’ ability to handle complicated, multi-dimensional datasets by using them to examine the gathered data. In detecting and categorizing sleep problems, the algorithm model outperforms the conventional diagnosis techniques. The comprehensive evaluation is provided by taking contextual elements like the surrounding surroundings and the sleep environment. The suggested method has promising use as a powerful tool for sleep problem diagnosis, and results from a large-scale clinical investigation confirm its efficacy. The system can improve assessment accuracy and personalized treatment plans. This paper provides a high-tech and realistic solution for doctors dealing with sleep disorders, which helps move the field forward in its diagnostic capabilities. The suggested system is an excellent option for home-based monitoring and extensive use in sleep clinics due to its scalability and flexibility, improving the treatment of sleep-related health problems in the long term.