# Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining
## 5 Conclusions  
In this study, we analyze the problem of smartphone addiction by applying a new technology using smartphone apps data. In particular, the difference between the current study and previous studies is that most of the existing studies were based on self-reporting surveys; thus, their results were not always satisfying owing to possible intentional manipulation or bias. Therefore, in this study, we analyzed smartphone usage patterns by combining both the self-reporting method and data mining techniques for more accurate results. Our results show that 6.4% of the participants where 9 out of 13 students were female students suffer from the smartphone addiction--high-risk use. In 2016, according to the National Statistical Office (NSO), only 22.5% of the students suffer from the smartphone group addiction, which is 158 based on our result (conducted in 2017), whereas 29.6% of the participating students suffer addiction from the 159 smartphone addiction risk group and smartphone addiction high-risk user group, which has 160 increased by 5.1% in only one year showing the danger of the overuse of smartphones on the youth. In addition, there was a considerable difference between the results of our study and the previous studies in terms of smartphone overuse. In 2016, the average weekly smartphone usage was 8.29 h, however, based on our obtained result, the average smartphone usage time is more than 6 h per day. In addition, most of the users turn on their smartphones more than 300 times unconsciously; more than 50% of the respondents said that they spend more than half of the day using smartphones either for learning or getting assistance in their daily lives. Thus, though smartphones seem to be an essential tool, their use also needs to be well-controlled. The self-reported questionnaire was classified into 13 items based on six criteria developed as the addictive scale for the behaviors of overuse. In this study, we used multiple linear regression analysis and decision tree analysis to determine which six factors are highly correlated with addiction. The following results were obtained. First, the higher the \"recurrence,\" the higher the addiction, and the higher the risk users, the higher the \"withdrawal,\" \"conflict,\" and \"salience\" order. Second, \"salience\" was the highest for potential-risk users and some \"tolerance\" items were included. Third, as a result of applying the variables that may affect the addiction, the higher the number of screen turns, the greater the difference between the actual use time and the perceived use time, the higher the risk users. Fourth, potential-risk users have a large difference in \"actual use time--perceived use time.\" Fifth, the high-risk users were unable to identify the actual smartphone usage time. Therefore, the results can be analyzed as follows. First, it can be interpreted that the variables \"number of screen turns\" and \"actual use time--perceived use time\" are more influential on poisoning than the previous research that used the smartphone as poisoning. Second, we propose that we weight all items according to the items rather than 1 to 5 points equally. Third, in the existing research results, the more the amount of usage and the time of use, the more detailed the factors and variables than the simple result of addiction. This study has a limit of 125 in the experimental group; thus, it is necessary to add more experimental groups. In the future, by combining the self-report methods and use of smartphone data and analyzing it using data mining, we can also analyze the danger of overuse, such as depression, anxiety, stress, self-esteem, and emotional regulation. **Author Contributions:** [PERSON] and [PERSON] conceived the ideas proposed in this paper and designed the research method; [PERSON] implemented the proposed method and performed the related experiments; [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the paper. **Funding:** This research received no external funding.