• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.17-21
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• 2023

The primary objective of this research is to utilize machine learning models to analyze factors tailored to each dataset for predicting mental health conditions. The study aims to develop appropriate models based on specific datasets, with the goal of accurately predicting mental health states through the analysis of distinct factors present in each dataset. This approach seeks to design more effective strategies for the prevention and intervention of depression, enhancing the quality of mental health services by providing personalized services tailored to individual circumstances. Overall, the research endeavors to advance the development of personalized mental health prediction models through data-driven factor analysis, contributing to the improvement of mental health services on an individualized basis.

• Journal of the Korean Home Economics Association
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• v.38 no.6
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• pp.149-169
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• 2000

The purpose of this study was to examine the environmental characteristics of adolescents in low-income families, identify the high-risk & protective factors among environmental contexts surrounding adolescents, and investigate the relative importance of high-risk & protective factors to adolescents'psychological and behavioral adjustment separately. The present study was the primary research of developing prevention program for adolescents'deviant behaviors in low-income families. Subjects of this study consisted of 176 adolescents drawn from 8 social-welfare institutions in Chungbuk province. The pilot study was done to examine the applicability of survey instrument. Data were analyzed by the frequency, percentage, Pearson correlation, stepwise regression using SPSS/WIN program. The results were as followings: 1. There was statistically correlated with each other in environmental high-risk and protective factors except an housing environment. The results implies that environmental contexts itself surrounding adolescents in low-income families can be either high-risk factors or protective factors. 2. The adolescents in low-income families perceived that stresses from consumer and school environments were high-risk factors among other environmental contexts. 3. The adolescents in low-income families perceived that resources from friend and school were protective factors among other environmental contexts. 4. The stresses from friend and eating behaviors were significant factors predicting adolescents'relative psychological adjustment. However, the behavioral adjustment was not predicted by environmental contexts. 5. The resources from school, consumer, and eating behaviors were significant factors predicting adolescents'relative psychological adjustment. Also, the resources from school, eating behavior, and family were predictors of behavioral adjustment. This research implies that the findings can be based on the development of prevention program for adolescents deviant behaviors in low-income families.

• Journal of Acupuncture Research
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• v.25 no.2
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• pp.189-197
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• 2008

Objectives : This study was performed in order to investigate the effectiveness of electromyography and the Heart Rate Variability(HRV) test as prognosis factors, and to clarify correlation between Electromyography and the Heart Rate Variability test. Methods : 44 Bell's palsy patients who were graded V on the House-Brackmann scale and underwent HRV and EMG testing were retrospectively reviewed based on medical records. Results from both tests were analyzed via simple linear regression, and bivariate correlation analysis was performed to investigate the correlation between results from the two tests. The severity of the facial palsy at onset and at 2 weeks after treatment were evaluated with the H-B grade and Yanagihara grading system, and was converted into improvement scores. Results : Mean axonal loss according to electromyography showed a statistically significant correlation in predicting peripheral facial palsy improvement(p<0.01). HR, SDNN, TP, LF, HF, VLF, and LF/HF ratio on the Heart Rate Variability test showed no significant correlation in predicting peripheral facial palsy improvement. Mean axonal loss determined by electromyography, and HR, SDNN, TP, LF, HF, VLF, and LF/HF ratio recorded with the Heart Rate Variability test was analyzed with the bivariate correlation analysis method. Mean axonal loss and SDNN showed a statistically significant correlation(p<0.01) Conclusions : The Heart Rate Variability test has no statistical significance in predicting peripheral facial palsy improvement. SDNN has a statistically significant correlation with mean axonal loss as determined by electromyography.

• Korean Journal of Human Ecology
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• v.17 no.2
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• pp.207-221
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• 2008

This study examined different individual, family factors and classroom environment that affect children's perceived competency. For an analysis, achievement motivation, intrinsic locus of control and anxiety were included in individual variables. For family factors, parental support and marital conflict were examined. For classroom psycho-social environment, teacher support, peer relations, classroom involvement and teacher control were used. The sample consisted of 565 fifth and sixth grade children. Statistics and methods used for the data analysis were Cronbach's alpha, Factor analysis, frequency, percentage, t-test, Pearson's correlation, and Hierarchical Regression. Several major results were found from the analysis. First, boy's perceived academic competency was higher than girl's. And no sex difference was in children's social and athletic competency. Second, boy's and girl's perceived academic and social competency and boy's perceived athletic competency had a positive correlation with achievement motivation, intrinsic locus of control, parental support, teacher support, peer relations and classroom involvement. And girl's perceived athletic competency had a positive correlation with achievement motivation, intrinsic locus of control, parental support and peer relations. But boy's and girl's perceived academic and social competency and boy's perceived athletic competency had a negative correlation with anxiety and parental marital conflict. Third, the most important variable predicting boy's and girl's perceived academic competency was achievement motivation. The most important variable predicting boy's and girl's perceived social competency was peer relations. And the most important variable predicting boy's perceived athletic competency was peer relations. On the other hand, the most important variable predicting girl's perceived athletic competency was father's support.

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.3
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• pp.46-54
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.

• Computers and Concrete
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• v.24 no.5
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• pp.399-412
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• 2019

In this investigation, Elman neural networks were utilized for predicting the mechanical properties of Self-Compacting Concretes (SCCs). Elman models were designed by using experimental data of many different concrete mixdesigns of various types of SCC that were collected from the literature. In order to investigate the effectiveness of the selected input variables on the network performance in predicting intended properties, utilized data in artificial neural networks were considered in two sets of 8 and 140 input variables. The obtained outcomes showed that not only can the developed Elman ANNs predict the mechanical properties of SCCs with high accuracy, but also for all of the desired outputs, networks with 140 inputs, compared to ones with 8, have a remarkable percent improvement in the obtained prediction results. The prediction accuracy can significantly be improved by using a more complete and accurate set of key factors affecting the desired outputs, as input variables, in the networks, which is leading to more similarity of the predicted results gained from networks to experimental results.

• Magazine of RCR
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• v.19 no.3
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• pp.41-45
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• 2024

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.44 no.4
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• pp.174-181
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• 2018

Objectives: The number of patients with medication-related osteonecrosis of the jaw (MRONJ) is increasing, but treatment remains controversial. Published papers and systematic reviews have suggested that surgical treatment is effective in patients with MRONJ. The purpose of this study was to determine whether preoperative University of Connecticut Osteonecrosis Numerical Scale (UCONNS), other serologic biomarkers, and size of necrosis are prognostic factors for outcome of surgical treatment in MRONJ. Materials and Methods: From January 2008 to December 2016, 65 patients diagnosed with MRONJ at the Department of Oral and Maxillofacial Surgery in College of Dentistry, Dankook University who required hospitalization and surgical treatment were investigated. Patient information, systemic factors, and UCONNS were investigated. In addition, several serologic values were examined through blood tests one week before surgery. The size of osteolysis was measured by panoramic view and cone-beam computed tomography in all patients. With this information, multivariate logistic regression analysis with backward elimination was used to examine factors affecting postoperative outcome. Results: In multivariate logistic analysis, higher UCONNS, higher C-reactive protein (CRP), larger size of osteolysis, and lower serum alkaline phosphate were associated with higher incidence of incomplete recovery after operation. This shows that UCONNS, CRP, serum alkaline phosphate, and size of osteolysis were statistically significant as factors for predicting postoperative prognosis. Conclusion: This study demonstrated that CRP, UCONNS, serum alkaline phosphate, and size of osteolysis were statistically significant factors in predicting the prognosis of surgical outcome of MRONJ. Among these factors, UCONNS can predict the prognosis of MRONJ surgery as a scale that includes various influencing factors, and UCONNS should be used first as a predictor. More aggressive surgical treatment and more definite surgical margins are needed when the prognosis is poor.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8447-8450
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• 2014

Background: To evaluate factors for predicting the granulosa cell tumor of the ovary (GCTO) pre-operatively. Materials and Methods: This retrospective designed study was conducted on 34 women with GCTO as the study group and 76 women with benign ovarian cysts as the control group. Data were recorded from the hospital database and included age, body mass index (BMI), parity, serum estradiol ($E_2$) levels, diameter of the mass, ultrasonographic features, serum CA125 level, risk of malignancy index (RMI), duration of menopause, postoperative histopathology result, and the neutrophil/lymphocyte ratio (NLR). Results: The demographic parameters showed no statistically significant difference between the groups. Preoperative diameter of the mass, CA125, duration of menopause, and neutrophil/lymphocyte ratio were significantly different between the groups. ROC curve analysis demonstrated that diameter of the mass, serum estradiol and Ca125 levels, RMI and NLR may be discriminative factors in predicting GCTO preoperatively. Conclusions: In conclusion, we think that a careful preoperative workshop including diameter of the mass, serum estradiol ($E_2$) and Ca125 levels, RMI and NLR may predict GCTO and may prevent incomplete approaches.