스마트폰 과의존 판별을 위한 기계 학습 기법의 응용
Application of Machine Learning Techniques for Problematic Smartphone Use
- 발행
- 2022 등록이 논문은 발행 시점을 확인하지 못해 원문 등록일을 적었습니다. KCI가 2005~2008년에 옛 논문을 몰아서 올린 탓에, 그 시기 등록분은 발행보다 평균 3~5년 늦습니다. 실제 발행연도는 더 이를 수 있습니다.
- 소속·발행
- 건국대학교
- 출처
- 국내 KCI
- DOI
- 10.32599/apjb.13.3.202209.293
- 원문
- 원문 보기 ↗
개념
키워드
Smartphone Overdependence, Problematic smartphone use, Machine learning, Predictor., Smartphone Overdependence, Problematic smartphone use, Machine learning, Predictor.
초록
Purpose - The purpose of this study is to explore the possibility of predicting the degree of smartphone overdependence based on mobile phone usage patterns.
 Design/methodology/approach - In this study, a survey conducted by Korea Internet and Security Agency(KISA) called “problematic smartphone use survey” was analyzed. The survey consists of 180 questions, and data were collected from 29,712 participants. Based on the data on the smartphone usage pattern obtained through the questionnaire, the smartphone addiction level was predicted using machine learning techniques. k-NN, gradient boosting, XGBoost, CatBoost, AdaBoost and random forest algorithms were employed.
 Findings - First, while various factors together influence the smartphone overdependence level, the results show that all machine learning techniques perform well to predict the smartphone overdependence level. Especially, we focus on the features which can be obtained from the smartphone log data (without psychological factors). It means that our results can be a basis for diagnostic programs to detect problematic smartphone use. Second, the results show that information on users’ age, marriage and smartphon