# Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining
## 3 Research Method
### Self-Report Data Collection  
Before collecting smartphone usage data, the KISA [17] standardized adult smartphone addiction self-diagnosis scale was used as the smartphone addiction self-diagnosis scale in our study. Our questionnaire consisted of four item categories with 15 questions (five, four, four, and two items for daily living disability, withdrawal, other times for tolerance, and virtual world orientation, respectively). Based on the score factors, the results of the questionnaire were classified into high-risk, potential risk, and general use. In addition, based on the study by [PERSON] [16], the smartphone-addiction scale classification was based on the following six criteria: salenines, mood control, tolerance, conflict, withdrawal, and recurrence. Table 2 lists the obtained results for self-reported addiction in our study.  
Among the total of 125 respondents (including 49% men and 51% women), 28%, 38%, 18%, and 16% of the participants were first-year, second-year, third-year, and final-year students, respectively. Furthermore, the questions were divided into many life-related topics; in particular, questions on social studies, humanities, engineering, natural sciences, medical sciences, and arts/physical education consisted 41%, 27%, 17%, 10%, 2%, and 3% of the questions, respectively.  
Our results show that 6.4% of the participants where 9 out of 13 students were female students suffer from smartphone addiction referred to as high-risk use. According to KNOSO, in 2016, only 22.5% of the students suffered from smartphone addiction; however, based on our study (which was conducted in 2017), 29.6% of the participating students were part of the smartphone addiction risk and smartphone addiction high-risk user groups, thus showing an increase by 5.1% in smartphone addiction only in one year, indicating the danger of smartphone overuse on the youth.  
In particular, grade-specific characteristics with high-risk were observed in the first- and second-year college students. There was no high-risk use for students achieving grade A, but 13.6% of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Addiction Group**} & \multirow{2}{*}{**All**} & \multicolumn{4}{c}{**Gender**} & \multicolumn{4}{c}{**Grade**} & \multicolumn{4}{c}{**Level**} \\ \cline{3-13}  & & **M** & **F** & **A** & **B** & **C** & **F** & **1** & **2** & **3** & **4** \\ \hline \multirow{2}{*}{High-risk user group} & \multirow{2}{*}{8} & \multirow{2}{*}{6.4\%} & 3.2\% & 7.2\% & 0\% & 3.2\% & 2.4\% & 0\% & 0.8\% & 4\% & 0\% & 1.6\% \\  & & & (4) & (9) & (0) & (4) & (3) & (0) & (1) & (5) & (0) & (2) \\ \hline \multirow{2}{*}{Potential-risk user group} & \multirow{2}{*}{29} & \multirow{2}{*}{23.2\%} & 15.2\% & 14.4\% & 6.4\% & 4\% & 5.6\% & 0\% & 6.4\% & 4.8\% & 4\% & 4\% \\  & & & (19) & (18) & (8) & (5) & (7) & (0) & (8) & (6) & (5) & (5) \\ \hline \multirow{2}{*}{General user group} & \multirow{2}{*}{88} & \multirow{2}{*}{70.4\%} & 28.8\% & 31.2\% & 21.6\% & 24.8\% & 30.4\% & 1.6\% & 21.6\% & 31.2\% & 10.4\% & 11.2\% \\  & & & (36) & (39) & (27) & (31) & (38) & (2) & (27) & (39) & (13) & (14) \\ \hline \multirow{2}{*}{Addiction risk group} & \multirow{2}{*}{37} & \multirow{2}{*}{29.6\%} & 18.4\% & 21.6\% & 6.4\% & 7.2\% & 8\% & 0\% & 7.2\% & 8.8\% & 4\% & 5.6\% \\  & & & (23) & (27) & (8) & (9) & (10) & (0) & (9) & (11) & (5) & (7) \\ \hline \multirow{2}{*}{Ratio} & \multirow{2}{*}{125} & \multirow{2}{*}{100\%} & \multirow{2}{*}{47.2\%} & 52.8\% & 28\% & 32\% & 38.4\% & 1.6\% & 28.8\% & 40\% & 14.4\% & 16.8\% \\ \hline \end{tabular}
\end{table}
Table 2: Respondent distribution based on personal characteristics (Unit: ratio (persons)).  
grade B students were at risk for addiction. Thus, by measuring smartphone usage, it is found that students with high class are included in the group with high use of smartphone.