A Comprehensive Review on Machine Learning Approaches for Smartphone Addiction Prediction

Smartphone addiction has become a pressing public health concern, with far-reaching implications for mental health, social interactions, and overall well-being. This study proposes an unsupervised machine learning framework to predict smartphone addiction by analyzing user behavior data, such as screen time, application usage patterns, and psychological factors. The research leverages unsupervised learning techniques to identify patterns and commonalities in unlabelled data, aiming to classify individuals at risk of addiction without predefined categories. Using advanced machine learning libraries like TensorFlow and OpenCV, the system incorporates a robust pipeline involving data collection, preprocessing, and training/testing with high-performance models. The findings highlight excessive social media use and notification frequency as significant predictors, with applications ranging from personalized intervention programs and mental health monitoring to enhanced parental control systems. With a focus on scalable and adaptive methodologies, this framework not only addresses the challenges of smartphone addiction but also offers valuable insights for researchers, policymakers, and app developers to design effective solutions for healthier technology use in an increasingly digital society