• Journal of Digital Convergence
• /
• v.13 no.1
• /
• pp.353-366
• /
• 2015

The purpose of this study was to examine how region-specific characteristics affect the occurrence of cardiac arrest. To analyze, we combined a unique data set including key indicators of health condition and cardiac arrest occurrence at the 244 small administrative districts. Our data came from two main sources in Korea Center For Disease Control and Prevention (KCDC): 2010 Out-of-Hospital Cardiac Arrest Surveillance and Community Health Survey. We analyzed data by using multiple regression, geographically weighted regression and decision tree. Decision tree model is selected as the final model to explain regional variations of cardiac arrest. Factors of regional variations of cardiac arrest occurrence are population density, diagnosis rates of hypertension, stress level, participating screening level, high drinking rate, and smoking rate. Taken as a whole, accounting for geographical variations of health conditions, health behaviors and other socioeconomic factors are important when regionally customized health policy is implemented to decrease the cardiac arrest occurrence.

• Korean Journal of Health Education and Promotion
• /
• v.22 no.4
• /
• pp.73-90
• /
• 2005

Objectives: Smokers who had failed to quit smoking were frequently reported that life stress mostly interrupted their abstention. Stress vulnerability model for smoking cessation has been considered, and most of contemporary smoking cessation programs help smokers develop coping strategies for stressful situations. This study aims to investigate the appropriate coping styles for stress of abstention from smoking. The result of investigating the relationship between abstention following smoking cessation program and coping styles would suggest useful information for those who want to stop smoking and health practitioners who help them. Methods: Participants were 69 smokers (62 males, 7 females) participated in a hospitalized smoking cessation program, whose mean age was 44.89 (SD=9.61). Participants took medical test and completed questionnaires and psychological tests including: Fagerstrom Test for Nicotine Dependence and Multidimensional Coping Scale. To identify participants' abstention, researchers followed them for 2 years. To identify whether abstained or not and encourage them to abstain, researchers called them on the telephone once a week for 3 months. After 3 months, they were contacted every other week till 6 months passed since they left smoking cessation program. And they were contacted once a month for other 18months. Researchers also contacted their family to identify their abstention. Data Mining Decision Tree was performed with 37 variables (13 variables for the coping styles and 24 smoking-related variables) by Answer Tree 3.0v Results: Forty four (63.8%) out of sixty nine for 2 weeks, 34 (49.3%) for 6 months, 25 (36.2%) abstained for 1 year, and 22 (31.9%) abstained for 2 years. Participants of this study abstained average of 286.77 days from smoking. Included variables of a Decision Tree model for this study were positive interpretation, emotional expression, self-criticism, restraint and emotional social support seeking. Decision Tree model showed that those (n=9) who did not interpret positively (<=7.5) and criticized themselves (>6.5) abstained 23 days only, while those (n=9) who interpreted positively (>7.5), expressed their emotion freely (>6.5), and sought social support actively (>11.5) abstained 730 days, till last day of the investigation. Conclusion: The results of this study showed that certain coping styles such as positive interpretation, emotional expression, self-criticism, restraint and emotional social support seeking were important factors for long-term abstention from smoking. These findings reiterate the role of stress for abstention from smoking and suggest a model of coping styles for successful abstention from smoking. Despite of limitation of this study, it might help smokers who want to stop smoking and health practitioners who help them.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.5
• /
• pp.21-28
• /
• 2022

All health problems that occur in the circulatory system are refer to cardiovascular illness, such as heart and vascular diseases. Deaths from cardiovascular disorders are recorded one third of in total deaths in 2019 worldwide, and the number of deaths continues to rise. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. In this study, models are produced to predict heart disease, which is one of the cardiovascular diseases, and compare the performance of models with Accuracy, Precision, and Recall, with description of the way of improving the performance of the Decision Tree(Decision Tree, KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and DNN (Deep Neural Network) are used in this study.). Experiments were conducted using scikit-learn, Keras, and TensorFlow libraries using Python as Jupyter Notebook in macOS Big Sur. As a result of comparing the performance of the models, the Decision Tree demonstrates the highest performance, thus, it is recommended to use the Decision Tree in this study.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.203-212
• /
• 2002

This study examined the predictive power of data mining algorithms by comparing the performance of logistic regression and decision tree algorithm, called CHAID (Chi-squared Automatic Interaction Detection), On the contrary to the previous studies, decision tree performed better than logistic regression. We have also developed a CDSS (Clinical Decision Support System) with three modules (doctor, nurse, and patient) based on data warehouse architecture. Data warehouse collects and integrates relevant information from various databases from hospital information system (HIS ). This system can help improve decision making capability of doctors and improve accessibility of educational material for patients.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.22 no.2
• /
• pp.49-57
• /
• 2019

The street trees increase the liveability of cities by reducing stormwater runoff, improving air quality, storing carbon, providing shade, and ameliorating the urban heat-island effect. In this study, the health status of street trees in Suwon was evaluated, and the factors affecting the growth of the trees were also derived. In order to evaluate the growth and health of street trees, field survey was carried out on a total of 125 trees in 25 sections of the Deogyeong-daero where is through the city. During the field survey, the following items were examined: Street trees health status (i.e. species, height, DBH (diameter at breast height), planting types, vigor, etc.), soil factors (i.e. soil temperature, humidity, pH, hardness, etc.), and environmental factors (i.e. landuse, road width, etc.). As the results of field survey, the main species of the street trees was Zelkova serrata, which was healthy in most of the sections. The factors such as planting types, soil temperatures, tree root cover, road extension, distance from the road were derived to affect the growth and health of street trees, and the differences were significant. The results of this study were derived the following conclusions for vigorous street trees: First, it is important to install and maintain the protection facilities like tree root cover for the growth of trees. Second, it is necessary to discuss how to plant multiple trees in narrow spaces like a street green space. Third, it is important to provide appropriate soil conditions continuously for growth of threes. Finally, it should be utilized as a mitigation measure of urban heat island effects.

• Korea Journal of Hospital Management
• /
• v.10 no.1
• /
• pp.71-92
• /
• 2005

The purpose of this study is to analyze the efficiency in management of general hospitals and investigate the major factors on efficiency. Specifically, the management of each general hospital is evaluated by using Data Envelopment Analysis(DEA) technique which is a nonparametric statistical method for measurement of efficiency. Then, the influencing factors are investigated through analyses of Decision-Tree Model and Tobit Regression. The target hospitals were general hospitals in which bed sizes are between 200 and 500 among a total of 276 general hospitals. The main data of financial indicators were collected from 48 hospitals, and it was analyzed by using two statistical models. For Model I, three input and two output variables were used for efficiency evaluation. In particular, three input variables were the number of medical doctors, the number of paramedical personnel, and the bed size. And, two output variables were the numbers of inpatients and outpatients per year, adjusted by bed-size. The results of DEA analysis showed that only seven out of 48 hospitals(15%) turned out to be efficient. The decision-tree analysis also showed that there were six significant influencing factors for Model I. Six factors for Model I were Bed Occupancy Rate, Cost per Adjusted Inpatient, New Visit Ratio of Outpatients, Retired Ratio, Net Profit to Gross Revenues, Net Profit to Total Assets. In addition, the management efficiency of hospital is proved to increase as profit and patient-induced indicators increase and cost-related indicators decrease, by the Tobit regression model of independent variables derived from the decision-tree analysis. This study may be contributable to the development of analytic methodology regarding the efficiency of hospital management in that it suggests the synthetic measures by utilizing DEA model instead of suggesting simple ratio-analyzing results.

• Journal of Korean Academy of Nursing
• /
• v.49 no.5
• /
• pp.575-585
• /
• 2019

Purpose: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. Methods: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. Results: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. Conclusion: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

• Korean Journal of Agricultural Science
• /
• v.49 no.4
• /
• pp.957-969
• /
• 2022

It is well known that the fruit of the vitamin tree (Hippophae rhamnoides L.) provides excellent anti-diabetic, antibacterial, immune regulation, anti-inflammatory, and anti-aging effects. In some countries including Europe the fruit has been added to certain foods to develop functional foods. The present research was carried out to elucidate the biological function of vitamin tree fruit powder added to fermented milk. It was found that there was an antioxidant effect of yoghurt made with vitamin tree fruit powder, and this effect was greater with increased incubation time and amount of vitamin powder, as shown by 1,1-diphenyl2-picrylhydrazyl (DPPH) and 2,2-anziobis (3-ehtylbenzothiazoline-6-sulfonic aicd) (ABTS) radical scavenging activities. The antibacterial effect of yoghurt containing vitamin tree fruit powder was shown to be effective against four pathogenic microorganisms, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Salmonela Typhimurium. In particular, yoghurt supplemented with 5% of vitamin tree fruit powder showed the best antibacterial effect. The yogurt containing the vitamin tree fruit powder significantly inhibited the expression of the anti-inflammatory cytokines interleukin (IL)-6 (yogurt [Y] + Hippophae rhamnoides L. powder [HP] and yogurt containing 5% Hippophae rhamnoides L. powder [HPY]) and IL-1β (HP, Y + HP and HPY) in a concentration-dependent manner among tumor necrosis factor (TNF)-α, IL-6, IL-1, and induced nitric oxide synthase (iNOS). Our results clearly demonstrated that vitamin tree fruit powder could be a good functional ingredient for improving health through yoghurt manufactured with vitamin tree.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.14
• /
• pp.5593-5596
• /
• 2014

Background: The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. Materials and Methods: The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Results: Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Conclusions: Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

• Journal of Forest and Environmental Science
• /
• v.28 no.2
• /
• pp.63-67
• /
• 2012

Crown ratio is the ratio of live crown length to tree height. It is often used as an important predictor variable for tree growth equation. It indicates tree vigor and is a useful parameter in forest health assessment. The objective of the study was to develop crown ratio prediction models for Tectona grandis. Based on the data set from the temporary sample plots, several non linear equations including logistics, Chapman Richard and exponential functions were tested. These functions were evaluated in terms of coefficient of determination ($R^2$) and standard error of the estimate (SEE). The significance of the estimated parameters was also verified. Plot of residuals against estimated crown ratios were observed. Although the logistic model had the highest $R^2$ and the least SEE, Chapman-Richard and Exponential functions were observed to be more consistent in their predictive ability; and were therefore recommended for predicting crown ratio in the stand.