Abstract:
Issues related to Sleep have become a big problem among all age Groups, As
It leads to long-term physical and mental health problems. This research
aims to classify medical conditions according to sleep patterns and lifestyle
behaviors associated with them using machine learning algorithms. A dataset
was collected that included sleep habits, psychological symptoms, side effects
and techniques for managing them. Then, the data was further enriched via
SMOTE in order to solve the class imbalance problem. The feature selection
algorithm that was used was called Mutual Information. Using Random
Forest as a classifier model achieved the highest accuracy of 97.7%, and next
is XGBoost at 96.44%, followed closely by Decision Tree with an accuracy of
96.03% and K-Nearest Neighbors with an accuracy of only 83%. Methods that
were used also investigated pathways to late sleep and coping mechanisms.
It is believed that this research provides an example of the promising scope
of supervised machine learning models in health prediction respect of sleep
factors and will be useful for people and patients to detect long-term sleep
related disorders using data-driven messages.
