Abstract:
Sleep is one of the basic needs of survival. Sleep helps to reactivate our bodies; it also helps
to build and preserve memory and boost immunity. Our body feels relaxed because of sleep.
It also relieves our extreme tiredness. Sleep helps in growth and development in children
and adolescents. But nowadays sleeping disorders have become a great concern in our
society mostly in the youth. Sleeping disorders are happening for so many ways, likestress, anxiety, depression or an underlying health condition etc. In this paper we propose
a machine learning solution to detecting the effect of sleeping disorders on mental health
of young people. Adolescents commonly experience sleep difficulties, such as chronic
insomnia and delayed sleep phase disorders, which have been demonstrated to adversely
impact mental health symptoms such as anxiety, depression, and other psychiatric
comorbidities. Using a mixture of youths’ self-reports and clinical diagnoses, several
machine learning models were developed to detect patterns associating sleep problems
with mental health signs among the young population. The method allows for early
identification of at-risk patients and timely intervention. The results show that the
machine learning models have a high performance of predicting mental health effects due
to types of sleep disorder. In addition, the study emphasizes certain sleep parameters that
have the greatest impact on mental health of young people. For this problem, there are so
many solutions. The research in the sleeping disorder area is really huge. There are a lot
of machine learning algorithms which were proposed to detect sleeping disorders. Firstly,
we apply the dataset preprocessing in our dataset. In this paper we are using some
machine learning algorithm to detect the sleeping disorder problem in youth.
