Machine learning model for predicting insomnia levels among university students

Abstract

Insomnia is a common and concerning issue among university students that can have a significant impact on their academic performance, physical and mental health. Early detection and treatment of insomnia is important to reduce its harmful effects. This study uses machine learning techniques to provide a novel approach of predicting the severity of insomnia among university students. Our research uses a dataset that was collected from a wide range of university students and includes demographic information, lifestyle variables and mental health indicators. In order to develop predictive models for insomnia levels, we utilize some of the popular machine learning algorithms, such as K-Nearest Neighbors, Support Vector Machine (SVM), Naïve Bayes, Linear Discriminant Analysis, Stochastic Gradient Descent, Extra Trees Classifier, AdaBoost Classifier, Ridge Classifier. All of the classifiers predict with high accuracy and Support Vector Machine (SVM) outperformed the other models with an excellent accuracy of 94.34%. The results show the effectiveness of machine learning models in accurately detecting insomnia severity among university students. We can also learn more about the factors that are strongly associated with insomnia by conducting qualitative research. The results of our study may be utilized to develop technology-based solutions that detect and assist students who are experiencing sleeplessness, which will improve their academic performance and general health.