Predictive modeling of smartphone addiction among university students in bangladesh:

Abstract

This study aims to develop a predictive model for smartphone addiction among university students in Bangladesh using machine learning techniques. The research encompasses data collection through a comprehensive survey, which included demographic information and responses to 25 questions designed to assess smartphone usage patterns and behaviors. A weighted composite score was calculated for each respondent to represent their level of addiction, which was then normalized for standardization. Various machine learning models were trained and evaluated, including logistic regression, decision trees, and support vector machines, among others. The logistic regression model demonstrated the highest accuracy of 97% in predicting smartphone addiction. The study also identified key factors associated with smartphone addiction, such as loneliness, depression, and poor sleep quality. Additionally, the research found that smartphone addiction had negative impacts on students' academic performance and social interactions. The findings of this study provide valuable insights for university administrators and policymakers to develop targeted interventions and support services to address the growing issue of smartphone addiction among university students in Bangladesh. The predictive model can be further refined and integrated into student well-being programs to identify and support those at high risk of addiction.