Harnessing Naive Bayes for Accurate Classification of Sleep Disorders: A Data-Driven Approach

The Naive Bayes method may be used to automate the classification of sleep disorders, which is an important tool for early identification. The program identifies patterns associated with various sleep phases by evaluating health and lifestyle data such as age, gender, job, and sleep habits. The Naive Bayes technique employs a probabilistic methodology, yielding quick and accurate predictions based on processed data. The system works dependably owing to rigorous data pre-processing and model evaluation, making it a lightweight and cost-effective option for resource-constrained applications. This method establishes the framework for more sophisticated diagnostic tools, with potential enhancements including the inclusion of real-time data from wearable devices to provide more comprehensive insights into sleep health.