(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2025)
A lot of people struggle with sleep disorders, and when these problems go undiagnosed, they can lead to serious health issues. Right now, doctors mostly rely on tests like polysomnography (PSG) and expert analysis to spot these disorders, but that process eats up time and resources. Plus, it’s not always consistent—different experts might interpret results in their own way. In this paper, we introduce a new method for detecting sleep disorders that uses multi-layered ensemble learning and smart data balancing. One big hurdle is that sleep disorder datasets are usually imbalanced—some conditions show up way more often than others. To tackle this, we use data balancing tools like SMOTE, ADASYN, and both random over-sampling and under-sampling. When you combine these with ensemble methods like stacking and boosting, you get much better results in terms of accuracy, sensitivity, and specificity. Our framework is all about making sleep disorder detection more reliable, automated, and accurate. The goal is to catch these disorders earlier and more effectively, so patients get the care they need sooner, and healthcare systems don’t get overwhelmed.