Enhancing Sleep Health and Lifestyle Using Machine Learning Based Approaches

Sleep disorders are prevalent and impactful medical conditions which affect a significant portion of the global population and exert profound implications on physical health, cognitive function, and overall well-being. This paper introduces an AI-based model to detect and understand patterns of sleep disorders using sleep disorder dataset. Various machine learning classifiers, such as logistic regression, naive Bayes, KNN, random forest, support vector machine, CatBoost, and AdaBoost, were employed for classification purposes. The findings reveal that the Random Forest classifier demonstrates superior performance, achieving an accuracy of 89.36% with the lowest loss of 0.106. Additionally, Random Forest also performed well in terms of other parameters such as the precision with 0.90, recall, and Fl score metrics with 0.89 each. The paper underscores the potential of AI-driven methodologies in advancing the understanding and diagnosis of sleep disorders, with Random Forest emerging as a promising classifier for accurate identification and classification.