• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.9-14
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• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.43 no.4
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• pp.365-371
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• 2017

This study was carried out to investigate the effect of lacquer tree (Rhus verniciflua) extract on ganodermanondiol (GN), tyrosinase and melanin biosynthesis inhibitor, biosynthesis in Ganoderma lucidum mycelia. In HPLC analysis, GN contents were significantly increased in G. lucidum mycelial extracts supplemented with of 1, 5, 10, and 15% lacquer tree extracts (LTE). In addition, G. lucidum mycelial extracts supplemented with LTEs which had no cytotoxicity activity against B16F10 cells, significantly inhibited melanogenesis in B16F10 cells. GN biosynthesis was facilitated by LTE. Taken together, we propose that G. lucidum mycelial extracts supplemented with LTE can be used as an effective ingredient of skin care products in the future.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.40-53
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• 2021

Purpose: The purpose of this study was to develop and compare the prediction model for suicide attempts by Korean adolescents using logistic regression and decision tree analysis. Methods: This study utilized secondary data drawn from the 2019 Youth Health Risk Behavior web-based survey. A total of 20 items were selected as the explanatory variables (5 of sociodemographic characteristics, 10 of health-related behaviors, and 5 of psychosocial characteristics). For data analysis, descriptive statistics and logistic regression with complex samples and decision tree analysis were performed using IBM SPSS ver. 25.0 and Stata ver. 16.0. Results: A total of 1,731 participants (3.0%) out of 57,303 responded that they had attempted suicide. The most significant predictors of suicide attempts as determined using the logistic regression model were experience of sadness and hopelessness, substance abuse, and violent victimization. Girls who have experience of sadness and hopelessness, and experience of substance abuse have been identified as the most vulnerable group in suicide attempts in the decision tree model. Conclusion: Experiences of sadness and hopelessness, experiences of substance abuse, and experiences of violent victimization are the common major predictors of suicide attempts in both logistic regression and decision tree models, and the predict rates of both models were similar. We suggest to provide programs considering combination of high-risk predictors for adolescents to prevent suicide attempt.

• Asian Journal of Business Environment
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• v.4 no.2
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• pp.5-11
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• 2014

Purpose - This study attempts to examine Islamic banking practices in Iran based on new scientific methods. Design, methodology, and approach - The study used financial ratios demonstrating healthy or non-healthy banks to assess the financial health of banks listed on the Tehran Stock Exchange. The assessment of these ratios with a decision tree as a non-parametric method for modeling is recommended to present this model. Information about the financial health of banks could affect the decisions of different groups of banks' financial report users including shareholders, auditors, stock exchanges, central banks, and so on. Results - The results of the study show that a decision tree is a strong approach for classifying Islamic banks in Iran. Conclusions - To date, several studies have been conducted in various countries on the topic of this study. Considering the importance of Islamic banking, this is one of the first studies in Iran the outcomes of the study may prove helpful to the Iranian economy.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.639-647
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• 2008

This study used the characteristics of the knowledge discovery and data mining algorithms to develop tailored hypertension follow up management model - hypertension care predictive model and hypertension care compliance segmentation model - for hypertension management using the Korea National Health Insurance Corporation database(the insureds’ screening and health care benefit data). This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and ensemble technique. On the basis of internal and external validation, it was found that the model performance of logistic regression method was the best among the above three techniques on hypertension care predictive model and hypertension care compliance segmentation model was developed by Decision tree analysis. This study produced several factors affecting the outbreak of hypertension using screening. It is considered to be a contributing factor towards the nation’s building of a Hypertension follow up Management System in the near future by bringing forth representative results on the rise and care of hypertension.

• Environmental Engineering Research
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• v.23 no.4
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• pp.430-441
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• 2018

The adverse health impact of air pollution is becoming more serious. The purpose of this study is twofold: One is to analyze the effect of air pollution and temperatures on human health by analyzing the number of deaths from cardiovascular disease in Seoul, Korea; the other is to determine what impact the location of a monitoring site has on the results of a health study. For this latter purpose, air pollution and temperature monitors are sited at three locations termed green, public, and residential. Then, a decision tree model is used to analyze factors linked with deaths occurring at each monitoring site. The results show that the environmental temperatures before death and the $PM_{2.5}$ concentrations on the day of death are highly linked with the number of deaths regardless of the monitoring location. However, results are most accurate with residential data. The results of this study can be used as base data for a similar analysis and ultimately, as a guide to minimize the health impact of air pollution.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.495-502
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• 2020

In this study, the prevalence of osteoporosis was predicted based on 10 independent variables such as age, weight, and alcohol consumption and 4 tree-based machine-learning models, and the performance of each model was compared. Also the model with the highest performance was used to check the performance by clearing the independent variable, and Area Under Curve(ACU) was utilized to evaluate the performance of the model. The ACU for each model was Decision tree 0.663, Random forest 0.704, GBM 0.702, and XGBoost 0.710 and the importance of the variable was shown in the order of age, weight, and family history. As a result of using XGBoost, the highest performance model and clearing independent variables, the ACU shows the best performance of 0.750 with 7 independent variables. This data suggests that this method be applied to predict osteoporosis, but also other various diseases. In addition, it is expected to be used as basic data for big data research in the health care field.

• Journal of Environmental Science International
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• v.31 no.12
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• pp.1039-1050
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• 2022

Forests are valuable natural resources for people living around the mountains. In particular, the comfortable feeling or healing is one of the most important benefits obtained from forests. This healing can be possible by many aspects of forests, including the landscape, natural sounds, anions, and pleasant aromas. We focused on the volatile organics from forest causing pleasant aromas, phytoncides. Twenty phytoncides were monitored from February to September in a national tree garden (BaekDoDaeGan SooMokWon). Five sites were monitored two times per month and 20 phytoncides were detected. Borneol showed the highest annual average concentration and the order of concentration was borneol > mycene > sabinene > limonene > α-pinene. The average phytoncide concentration was relatively high in spring and summer season when the trees were physiologically active. Daily monitoring showed that the afternoon hours had higher concentrations of phytoncides than the morning hours, which may be due to the stabilized atmospheric conditions at the sites. Among the five sites, coniferous forests gave higher phytoncide emissions than broadleaf tree forests. The current study showed that forests produce several phytoncides that cause a healing effect and a forest bath may be beneficial to the health of visitors to forests.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.419-429
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• 2015

We investigate how the regional prevalence of hyperlipidemia is affected by health-related and socioeconomic factors with a special emphasis on geographic variations. We focus on the likelihood of hyperlipidemia as function of various region-specific attributes. We analysis a data set at the level of 249 small administrative districts collected from 2012 Korean Community Health Survey by Korea Centers for Disease Control and Prevention. To estimate, we use several methods including correlation analysis, multiple regression and decision tree model. We find that the average prevalence of hyperlipidemia in 249 small districts is 9.6% and its coefficient of variation is 28.3%. Prevalence of hyperlipidemia in continental and capital regions is higher than in southeast coastal regions. Further findings using decision tree model suggest that variations of hyperlipidemia prevalence between regions is more likely to be associated with rate of employee, level of stress, prevalence of hypertension, angina pectoris, and osteoarthritis in their regions.

• Proceedings of The Korean Society of Health Promotion Conference
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• 2005.11a
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• pp.120-122
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• 2005