Optimizing Sleep Disorder Classification: A Synergy Of Bayesian Optimization And Gradient Boosting Classifier

The categorization of sleep disorders with proper accuracy and efficiency is important in diagnosing and treatment, by considering the effect these conditions have on people’s lifestyle. This research hand out an innovative method for classifying sleep disorders by applying machine learning techniques, with Gradient Boosting Classifier (GBC) on prior optimized through Bayesian techniques. The extensive dataset encompassing various health-related characteristics, including lifestyle factors and patterns, which undergoes several preprocessing procedure to handle missing values and errors, thus ensuring data consistency and integrity. To establish a performance standard, a preliminary baseline model with the default parameters is made using GBC. The GBC’s hyperparameters are then modified using Bayesian Optimization, which aims to enhance the classification accuracy by analyzing the parameter space. The model’s performance is significantly improved by the optimization techniques, which can be seen by the increase in classification accuracy from 92.92% to 94.44%. This improvement shows that Bayesian Optimization works to refine complex machine learning models which in turn provides more reliable and accurate classification results. Compared to traditional machine learning algorithms while offering a more advanced diagnostic tool. These study results shows that the combination of Bayesian Optimization and GBC offers an effective method for accurately identifying sleep disorders.