• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.9-17
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• 2017

The student dropout prediction is an indispensable for many intelligent systems to measure the educational system and success rate of all university. Therefore, in this paper, we propose an intelligent dropout prediction system that minimizes the situation by adopting the proactive process through an effective model that predicts the students who are at risk of dropout. In this paper, the main data sets for students dropout predictions was used as questionnaires and university information. The questionnaire was constructed based on theoretical and empirical grounds about factor affecting student's performance and causes of dropout. University Information included student grade, interviews, attendance in university life. Through these data sets, the proposed dropout prediction model techniques was classified into the risk group and the normal group using statistical methods and Naive Bays algorithm. And the intelligence dropout prediction system was constructed by applying the proposed dropout prediction model. We expect the proposed study would be used effectively to reduce the students dropout in university.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1255-1262
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• 2021

The proportion of students dropping out of universities is increasing year by year, and they are trying to identify risk factors and eliminate them in advance to prevent dropouts. However, there is a problem in the management of students at risk of dropping out and the forecast is inaccurate because crisis students are managed through the univariable analysis of specific risk factors. In this paper, we identify risk factors for university dropout and analyze multivariables through machine learning method to predict university dropout. In addition, we derive the optimization method by evaluation performance for various prediction methods and evaluate the correlation and contribution between risk factors that cause university dropout.

• Nutrition Research and Practice
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• v.8 no.1
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• pp.94-102
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• 2014

Diet attrition and failure of long term treatment are very frequent in obese patients. This study aimed to identify pre-treatment variables determining dropout and to customise the characteristics of those most likely to abandon the program before treatment, thus making it possible to modify the therapy to increase compliance. A total of 146 outpatients were consecutively enrolled; 73 patients followed a prescriptive diet while 73 followed a novel brief group Cognitive Behavioural Treatment (CBT) in addition to prescriptive diet. The two interventions lasted for six months. Anthropometric, demographic, psychological parameters and feeding behaviour were assessed, the last two with the Italian instrument VCAO Ansisa; than, a semi-structured interview was performed on motivation to lose weight. To identify the baseline dropout risk factors among these parameters, univariate and multivariate logistic models were used. Comparison of the results in the two different treatments showed a higher attrition rate in CBT group, despite no statistically significant difference between the two treatment arms (P = 0.127). Dropout patients did not differ significantly from those who did not dropout with regards to sex, age, Body Mass Index (BMI), history of cycling, education, work and marriage. Regardless of weight loss, the most important factor that determines the dropout appears to be a high level of stress revealed by General Health Questionnaire-28 items (GHQ-28) score within VCAO test. The identification of hindering factors during the assessment is fundamental to reduce the dropout risk. For subjects at risk, it would be useful to dedicate a stress management program before beginning a dietary restriction.

• Journal of the Korean Society of School Health
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• v.34 no.3
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• pp.151-160
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• 2021

Purpose: The purpose of this study was to identify predictors of suicidal attempts in adolescents over 5 years after school dropout. Methods: The data of the Panel Survey of School Dropouts (of 2013 to 2017) conducted by the National Youth Policy Institute were analyzed. The analysis used the 2013 survey data as the baseline and examined suicidal attempts from 2013 to 2017. A total of 776 adolescents were included in the analysis. Descriptive statistics, 𝝌² test, t-test, and multiple logistic regression were carried out using SAS 9.2. Results: About 11% (87 out of 776) of the adolescents with an experience of dropout attempted suicide between 2013 and 2017. The risk of suicidal attempts was significantly lower in female (AOR: 0.57, 95% CI: 0.87~0.93) than in male adolescents. The higher the self-esteem, the lower the risk of suicidal attempts (AOR: 0.87. 95% CI: 0.78~0.97). The higher the depression level (AOR: 1.10, 95% CI: 1.05~1.16) and the rate of parental abuse (AOR: 1.09, 95% CI: 1.02~1.18), the higher the risk of suicidal attempts. Conclusion: The findings of the study suggest that those who are male, depressed, have low self-esteem or have been abused by their parents are at high risk of suicidal attempts among the adolescents with dropout experiences. Therefore, early intervention is necessary for those at high risk.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.277-290
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• 2015

The main purpose of study is to identify relevance between nature and types of risk factors that delinquent teenagers are exposed and types of methodologies implemented to prevent committing school violence, domestic violence, and suicide or to help recovering from violent activities and suicide attempts. The results show that school dropout has much relevance in risk factors such as probation, lawbreaking, smoking, drinking, runaway, domestic violence victim, and suicidal attempt. Risk rate of school dropout for those teenagers who smoke and drink in the period of runaway is 2.76 times higher than those teenagers who do not smoke or drink. More specifically, drinking increases more risk of school dropout than smoking. Contribution of this study is to identify empirical evidence that calls for comprehensive risk management for delinquent teenagers encompassing home, school, and community rather than focusing on risk itself.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.703-713
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• 2002

In longitudinal studies, outcomes are repeatedly measured over time for each subject. It is common to have missing values or dropouts for longitudinal data. In this study time trend in longitudinal data with dropouts is of concern. The confounding of time trend with dropout process is investigated through simulation studies. Some simulation results are reported for binary responses as well as continuous responses with patterns of dropouts varying. It has been found that time trend is not confounded with random dropout process for binary responses when it is estimated using GEE.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.

• Korean Journal of Acupuncture
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• v.35 no.1
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• pp.41-45
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• 2018

Objectives : To investigate minimum pressure by verifying changes in pressure due to bleeding amount during bloodletting-cupping therapy. Methods : (1) We compared adhesion performance of four different cupping cups of same size: two disposable cupping cups(A, B) and two reusable cupping cups(A, B) each were vacuumed three times and kept in place for 10 minutes. (2) We vacuumed two different sized disposable cupping cups(A), size.1(InnerDiameter 48.8 mm) and size.3(InnerDiameter 39.1 mm), twice each(-200 mmHg) on silicon plate. We injected water and air at regular intervals in cupping cups by using a syringe, and then measured change of pressure in cupping cups and pressure at the time of dropout. Results : (1) Pressure reduction was $4.75{\pm}2.78%$ on average in the order of 'Disposable[A]>reusable[B]>Disposable[B]>reusable[A]', so that pressure retention performance of disposable cups can't be regarded as inferior to that of reusable cups. (2) Pressure of disposable cupping B(size.1) decreased by an average of -40.08 mmHg per 5 ml of water. At -24.8 mmHg, when 22 ml of water has been injected, cup has come off. Pressure of disposable cupping B(size. 3) decreased by an average of -99.4 mmHg per 5 ml of water. At -48.6 mmHg, when 13 ml of water was injected, cupping came off. Conclusions : Considering reduction rate of pressure due to water injection, in case of bleeding more than 15 ml, size.3 cup always comes off, therefore it needs to be re-operated at least once. Meanwhile, size.1 cup does not always come off in the same condition, depending on the initial pressure and therefore, re-operation may be considered.

• Journal of Acupuncture Research
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• v.38 no.1
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• pp.41-46
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• 2021

Background: Pressure changes related to temperature variation during cupping may lead to dropout. This study aimed to investigate pressure changes related to temperature variations in the cup during the cupping procedure. Methods: Changes in temperature and pressure were measured for 15 minutes after the procedure was performed using the alcohol rub method with glass cups and with the addition of infrared irradiation. Changes in temperature and pressure were also measured for 15 minutes after pumping 3 times using the valve suction method, and with the addition of infrared irradiation. Results: In a comparison between the alcohol rub method with glass cups and with the addition of infrared irradiation, the negative pressure increased over time in the absence of infrared irradiation, whereas it decreased when performed with infrared irradiation p = 0.094. However, in a comparison between pumping 3 times using the valve suction method, and with the addition of infrared irradiation, the negative pressure decreased in both cases, but this was more significant with infrared irradiation p = 0.172. There was a significantly higher temperature in the glass cups (p = 0.004) and the valve cups (p = 0.001) exposed to infrared radiation, compared with no infrared irradiation. Conclusion: The reduction in negative pressure inside the cups exposed to infrared radiation was greater than without infrared irradiation. Temperature increases inside the cup can lead to the risk of dropout.