Construction of influencing factor segmentation and intelligent prediction model of college students' cell phone addiction model based on machine learning algorithm.

Mobile phone addiction among college students has emerged as a prevalent phenomenon in contemporary society, posing significant challenges to the development and well-being of these individuals. The assessment of the extent of mobile phone addiction has become an urgent concern in the present context. This study employed a sample of 3000 college students from a public university in Zhejiang Province, China, to gather questionnaire data. By utilizing a machine learning algorithm, we identified the most salient factors associated with college students' addiction, with perfectionism emerging as the primary influencer. Additionally, a machine learning-based prediction model for college students' cell phone addiction was developed, yielding a prediction accuracy of 76.68%. This intelligent model can serve as a reliable tool for subsequent evaluations of college students' cell phone addiction.