Classification of Sleep-Related Disorders Using Machine Learning Techniques

Several people suffer from irregular sleep patterns, a problem that negatively affects their daily lives. It presents a multitude of challenges, including daytime distress, disturbances in sleep-wake patterns, feelings of unease, and a variety of other complications. Diabetes, obesity, and psychological disorders are among the diseases that may be responsible for this sleep disorder. Therefore, the primary aim of this research is to employ the robust capabilities of machine learning in forecasting sleep disorders. This research classifies three diseases: diabetes, obesity, and psychological disorders, using thirty-two different classifiers belonging to five learning strategies. The research compares and evaluates their predictive performance on four datasets. The comparative analysis includes four evaluation metrics: Accuracy, Precision, ROC Area, and MCC. The research results indicate that the best classifier is SGD, followed by LMT, while NBM is the weakest classifier. In addition, the research results revealed that the best strategy is Functions, followed by Rules, with Bayes being the least effective strategy. While conducting the experiment and analyzing the data, it is observed that the best feature selection method that yielded the most favorable outcome is the CorrelationAttributeEval. Furthermore, it has been found that the classification algorithm known as SGD achieved the highest Accuracy and Precision in categorizing the data. This indicates that the combination of CorrelationAttributeEval as the feature selection approach and SGD as the classification algorithm proved to be the most successful and efficient strategy for this research