## Materials and Methods
### Data Set  
The collection of data is a crucial component of model development. There are a limited number of data collection methods that are entirely dependent on the type of research being conducted. Observation, interview, document scanning, measurement, questionnaire, or a combination of these methods may be used to collect data [15]. In our work, data was collected from a questionnaire prepared by the authors. According to the division of registration at Mahasarakham University, there were 37,579 undergraduate students registered for the second semester of the 2019 academic year. The sample size was calculated using [PERSON]'s pre-made table, which established the population's proportion of desirable qualities, 0.5% acceptable tolerance levels, and 95% confidence intervals for 380 individuals. The survey was conducted in the period between June 2020 and March 2021. The data set consisted of the responses of 460 participants. The inclusion criteria for this study were: either male or female, outpatients, aged more than 18 years, diagnosed without mood disorders, and volunteers who did not know they had depression. Participants were excluded if they were under the age of 18 years, illiterate, enrolled in other trials, or were in situations that did not allow obtaining written informed consent. Participants were not paid. The contents of the monitoring interviews were reviewed to identify patients who had attended at least 2 appointments. These criteria yielded the 380 participants  
Among the 380 participants in the accumulated dataset, 296 participants were found to be at risk for depression. The prevalence of depression among the participants of the survey was 77.9%. Table 1 shows the distribution of depressed and non-depressed participants in the dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Participants** & **Percentage (\%)** \\ \hline Depressed & 296 & 77.9 \\ \hline Non-depressed & 84 & 22.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of depressed and non-depressed dataset participants. Data in Table 1 are represented with imbalanced data, which is common when predicting with big data. Raw data are directly analyzed without using supplementary techniques such as a sample algorithm for data sets with imbalanced class ratios. This can decrease the performance of machine learning by causing prediction errors. Imbalanced data are data in which certain groups are overrepresented. One of the groups has a large amount of data, separated into majority and minority classes. Imbalanced information will result in the incorrect or less accurate categorization of the data of the minority group. However, it is possible to categorize the data of the majority group more precisely. General information can contain unbalanced information. In order to improve the data before analyzing them, the Synthetic Minority Over-sampling Technique (SMOTE) [54, 55, 56, 57] and resampling with replacement (bootstrapping) [58, 59, 60] were used in this study. With SMOTE, numerous variations exist for an oversampling technique that generates synthetic data by approximating minority instances. It is a type of oversampling method that has been demonstrated to be effective and is frequently utilized in machine learning with imbalanced high-dimensional data, and is increasingly utilized in the medical field. As an alternate way of assessing mediation, bootstrapping is a non-parametric resampling procedure that does not assume the normality of the sampling distribution. Bootstrapping is a computationally demanding method that estimates the indirect effect of each resampled data set by repeatedly sampling from the data set. In addition, as part of the preprocessing, the interviewer's pauses and background noise were deleted. This study prepared the data for sampling using the following methods: Table 2 shows the original data (D-ORI), the minority oversampling data (D-SM), and the resample with replacement data (D-RE).