A Machine Learning Based Approach to Detect the Impact of Sleeping Disorders on Mental Health in Youth

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.