• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.18 no.1
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• pp.11-24
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• 2014

Objectives The aim of this study is to contribute to the establishment of the Traditional Korean Medicine (TKM) policies in future, which is through the assessment to predict the realization by diagnostic subjects. Methods First, we evaluated 8 subjects that were deduced by professionals in 1996 regarding whether or not to be realized in 2013. Second, the governmental and private research projects, reports, articles, domestic patents and products were reviewed and investigated. Third, the Subjects in domestic fields of TKM were investigated on the followings: importance, time of realization, domestic Research and Development level, principal agents and methods for the realization, and hindrance factor on the realization. Results Of the 8 forecasting subjects, one subject was realized, two subjects were partly realized and five subjects were unrealized. Thus, their realization rate was 12.5%. The realized subject is the 'Standard naming of the TKM diagnosis'. Conclusion Continuous researches are necessary to realize the TKM subjects and moreover, professionals should predict new feasible TKM subjects, based on this study.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.91-103
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• 2018

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

• Nutrition Research and Practice
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• v.11 no.1
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• pp.51-56
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• 2017

BACKGROUND/OBJECTIVES: The purpose of this study was to assess the accuracy of a dietary reference intake (DRI) predictive equation for estimated energy requirements (EER) in female college tennis athletes and non-athlete students using doubly labeled water (DLW) as a reference method. MATERIALS/METHODS: Fifteen female college students, including eight tennis athletes and seven non-athlete subjects (aged between 19 to 24 years), were involved in the study. Subjects' total energy expenditure (TEE) was measured by the DLW method, and EER were calculated using the DRI predictive equation. The accuracy of this equation was assessed by comparing the EER calculated using the DRI predictive equation ($EER_{DRI}$) and TEE measured by the DLW method ($TEE_{DLW}$) based on calculation of percentage difference mean and percentage of accurate prediction. The agreement between the two methods was assessed by the Bland-Altman method. RESULTS: The percentage difference mean between the methods was -1.1% in athletes and 1.8% in non-athlete subjects, whereas the percentage of accurate prediction was 37.5% and 85.7%, respectively. In the case of athletic subjects, the DRI predictive equation showed a clear bias negatively proportional to the subjects' TEE. CONCLUSIONS: The results from this study suggest that the DRI predictive equation could be used to obtain EER in non-athlete female college students at a group level. However, this equation would be difficult to use in the case of athletes at the group and individual levels. The development of a new and more appropriate equation for the prediction of energy expenditure in athletes is proposed.

• Journal of The Korean Association For Science Education
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• v.32 no.3
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• pp.524-531
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• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.

• Korean Journal of Community Nutrition
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• v.19 no.6
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• pp.568-580
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• 2014

Objectives: The purpose of this study was to measure the resting metabolic rate (RMR) and to assess the accuracy of RMR predictive equations for Korean farmers. Methods: Subjects were 161 healthy Korean farmers (50 males, 111 females) in Gangwon-area. The RMR was measured by indirect calorimetry for 20 minutes following a 12-hour overnight fasting. Selected predictive equations were Harris-Benedict, Mifflin, Liu, KDRI, Cunningham (1980, 1991), Owen-W, F, FAO/WHO/UNU-W, WH, Schofield-W, WH, Henry-W, WH. The accuracy of the equations was evaluated on the basis of bias, RMSPE, accurate prediction and Bland-Altman plot. Further, new RMR predictive equations for the subjects were developed by multiple regression analysis using the variables highly related to RMR. Results: The mean of the measured RMR was 1703 kcal/day in males and 1343 kcal/day in females. The Cunningham (1980) equation was the closest to measured RMR than others in males and in females (males Bias -0.47%, RMSPE 110 kcal/day, accurate prediction 80%, females Bias 1.4%, RMSPE 63 kcal/day, accurate prediction 81%). Body weight, BMI, circumferences of waist and hip, fat mass and FFM were significantly correlated with measured RMR. Thus, derived prediction equation as follow : males RMR = 447.5 + 17.4 Wt, females RMR = 684.5 - 3.5 Ht + 11.8 Wt + 12.4 FFM. Conclusions: This study showed that Cunningham (1980) equation was the most accurate to predict RMR of the subjects. Thus, Cunningham (1980) equation could be used to predict RMR of Korean farmers studied in this study. Future studies including larger subjects should be carried out to develop RMR predictive equations for Korean farmers.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.1
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• pp.57-65
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• 2008

In the vehicle design, the research on driving posture has stood out as one of the important issues. Recently, the research on 3D human modeling focused on more exact implementation of real driving posture. However, prediction of driving posture through the 3D human modeling fail to reflect on the model the phenomenon called sagging, which refers to the retraction or shrinking of the torso while driving. 30 male subjects participated in the experiment where total subjects were divided into four groups according to height percentile(under 50%ile, 51%ile to 75%ile, 76%ile to 95%ile, over 95%ile). The independent variables were seat back angle(4 levels) and seat pan angle(2 levels). The dependent variable was capacity or the degree of retraction of the torso. First this study measured the sagging capacity by using a paired T-test between erect and retracted posture. Secondly it was tried to find out significant anthropometric variables that were statistically correlated by the analysis of correlation. Finally, a prediction model was derived which explains the capacity of sagging.

• Nutrition Research and Practice
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• v.9 no.1
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• pp.71-78
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• 2015

BACKGROUND/OBJECTIVES: The purposes of this study were to compare total energy expenditure (including PAL and RMR) of Korean farmers between the farming season and off farming season and to assess the accuracy of estimated energy requirement (EER) prediction equation reported in KDRIs. SUBJECTS/METHODS: Subjects were 72 Korean farmers (males 23, females 49) aged 30-64 years. Total energy expenditure was calculated by multiplying measured RMR by PAL. EER was calculated by using the prediction equation suggested in KDRIs 2010. RESULTS: The physical activity level (PAL) was significantly higher (P < 0.05) in the farming season (male $1.77{\pm}0.22$, female $1.69{\pm}0.24$) than the off farming season (male $1.53{\pm}0.32$, female $1.52{\pm}0.19$). But resting metabolic rate was significantly higher (P < 0.05) in the off farming season (male $1,890{\pm}233kcal/day$, female $1,446{\pm}140kcal/day$) compared to the farming season (male $1,727{\pm}163kcal/day$, female $1,356{\pm}164kcal/day$). TEE ($2,304{\pm}497kcal/day$) of females was significantly higher in the farming season than that ($2,183{\pm}389kcal/day$) of the off farming season, but in males, there was no significant difference between two seasons in TEE. On the other hand, EER of male and female ($2,825{\pm}354kcal/day$ and $2,115{\pm}293kcal/day$) of the farming season was significantly higher (P < 0.05) than those ($2,562{\pm}339kcal/day$ and $1,994{\pm}224kcal/day$) of the off farming season. CONCLUSIONS: This study indicates that there is a significant difference in PAL and TEE of farmers between farming and off farming seasons. And EER prediction equation proposed by KDRI 2010 underestimated TEE, thus EER prediction equation for farmers should be reviewed.

• Nutrition Research and Practice
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• v.9 no.4
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• pp.370-378
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• 2015

BACKGROUND/OBJECTIVES: Athletes generally desire changes in body composition in order to enhance their athletic performance. Often, athletes will practice chronic energy restrictions to attain body composition changes, altering their energy needs. Prediction of resting metabolic rates (RMR) is important in helping to determine an athlete's energy expenditure. This study compared measured RMR of athletic and non-athletic adolescents with predicted RMR from commonly used prediction equations to identify the most accurate equation applicable for adolescent athletes. SUBJECTS/METHODS: A total of 50 athletes (mean age of $16.6{\pm}1.0years$, 30 males and 20 females) and 50 non-athletes (mean age of $16.5{\pm}0.5years$, 30 males and 20 females) were enrolled in the study. The RMR of subjects was measured using indirect calorimetry. The accuracy of 11 RMR prediction equations was evaluated for bias, Pearson's correlation coefficient, and Bland-Altman analysis. RESULTS: Until more accurate prediction equations are developed, our findings recommend using the formulas by Cunningham (-29.8 kcal/day, limits of agreement -318.7 and +259.1 kcal/day) and Park (-0.842 kcal/day, limits of agreement -198.9 and +196.9 kcal/day) for prediction of RMR when studying male adolescent athletes. Among the new prediction formulas reviewed, the formula included in the fat-free mass as a variable [$RMR=730.4+15{\times}fat-free\;mass$] is paramount when examining athletes. CONCLUSIONS: The RMR prediction equation developed in this study is better in assessing the resting metabolic rate of Korean athletic adolescents.

• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.1-17
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• 2004

We guarantee the significance of the provided prediction model and predicted figures from the experts consulting group and we product the prediction figures of the domestic e-commerce market size in future by business subjects, BtoB, BtoG and BtoC. Besides, we do predict by the high raked 6 merchandises in the case of BtoC market size prediction. We use the KNSO(Korea National Statistical Office) BtoB, BtoG and BtoC data to ensure the significance of data.