Sleep disorders, both emotional and physical, present significant challenges, including daytime distress, sleep-wake disorders, and anxiety. A recent study harnesses machine learning to predict these disorders, offering new hope for those affected.

The primary goal was to leverage machine learning for accurate sleep disorder prediction. The study identified the most effective regression and classification models and learning strategies by analyzing datasets and evaluation metrics. The Multilayer Perceptron, SMOreg, and KStar models excelled in regression tasks, while IBK, RandomForest, and RandomizableFilteredClassifier models outperformed others in classification. The Function learning strategy proved superior across datasets.

Sleep is vital for brain function and physical health, supporting growth and impacting cognitive and emotional stability. Sleep disorders increase hunger, reduce physical activity, and raise risks of obesity, stroke, and heart disease. They also cause stress and fatigue, with sleep apnea affecting millions globally. Up to 30% of the world’s population suffers from sleep disorders, with a higher prevalence among women.

The study highlighted key trends, such as the impact of COVID-19 on sleep, the need for affordable tests beyond obstructive sleep apnea (OSA), and the use of machine learning and wearable devices for home-based diagnosis.

This research offers valuable insights into using machine learning for sleep disorder prediction. It suggests that machine learning combined with wearable devices can enable accurate at-home diagnosis, making monitoring more accessible and affordable. This marks a significant advancement in addressing sleep disorders, improving the quality of life and health for those affected.