• Journal of agricultural medicine and community health
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• v.24 no.1
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• pp.35-47
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• 1999

Medication non-compliance among the elderly results in medical problems and substantial cost to the health care system. This study investigate predicted variable related to the medication task ability among elderly. This study was done in the selected 4 villages in Kimchun County of Kyungbuk Province from July to August, 1996. The subject was the resident that 202 adults above 60 years of age. The questionnaire of interview included medication task ability, socio-demographic data, COOP/WONCA chart, family ABGAR score. BDI(Beck depression inventory), ADL(activities of daily living), IADL(instrumental activities of daily living), and MMSE-K(minimental state examination-Korean version). The results were as followed : 1. Approximately 49% of study population was taking drug medication currently. We found that 93% of study population was successful at the medication task all alone, 6% was failure at the medication task all alone, so need help partly or completely. 2. Significant variables between group of medication task ability were age, educational attainment, IADL, and MMSE-K in univariate analysis. And significant correlated variables with medication task ability were ADL, IADL, MMSE-K, and BDI in correlation analysis. 3. Major predictors to medication task ability on multiple logistic regression were IADL and sex finally. Findings suggest that IADL is related to medication task ability than other test battery of health status, so IADL could be used to necessary for medication management and add information to conventional methods of assessing mental status.

• Korea Journal of Hospital Management
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• v.7 no.3
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• pp.44-62
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• 2002

Advanced surgical technology demands more precise, meticulous, and time-consuming procedures. In addition, the patient's preference of tertiary health providers makes over crowding of the University Hospitals. Therefore, it has been necessary to maximize utilization of the operating room of such hospitals to accommodate these requirements. This study, targeting 1,302 surgical cases performed in 22 operating rooms at a university hospital in Seoul from October 8 to November 1, 2001, analyzed reasons for delay, and factors that caused delayed use of operating rooms. This study also assessed that the rate of operating room use would increase if the sources for possible reform were improved. 1. Among total of 1,302 cases of surgery, the incidence of surgeries in which there were no time delays and no factors for delay were discovered is 71.4% or 930 cases: the incidence in which surgeries were delayed was 28.6% or 372 cases. 2. As results of logistic regression for delay, procedures involving women were delayed 1.4 times more frequently than those of men. Compared to Department A, Department B was 1.8 times more likely to be delayed, and Department H was 0.4 times less likely to be delayed. Regional anesthesia was 2.4 times more likely to be delayed than general anesthesia, and surgeries that PCA was applied were 0.6 times less likely to be delayed than those when it was not. Surgeries performed on the Thursday were 1.7 times more likely to be delayed than those performed on the Monday. Compared to surgeries performed between 07:00-07:59, those performed between 08:00-08:29 were 4.3 times higher. 3. The reasons for delay were related to surgeon, surgical department, patient, anesthesia, administrative system, sick ward, and support services. Among these, 5,755 minutes for 276 delayed cases could be resolved easily, and resolving delays of 3,320 minutes for 131 cases would be more difficult. Among the causes for delay that could be improved, delays due to patient's transfer and surgeon's factor were the most common, 21.6% and 17.4% respectively. 4. If resolvable delays are improved, pre-anesthesia room is administered, and regional anesthesia and PCA are done ahead of time, use of emergency operating rooms will increase, we can increase overall utilization by 4.09%, we will save 744 minutes a day, we can reduce the time the operation room is used after 4 PM by 35%, and we can resolve the operation cancellations due to insufficient operating rooms. For the increase in the use of operating rooms, we need to maximally decrease the delays that could be improved, by allocating block time based on used totals hours of elective cases, giving accurate information on surgery schedule, voluntary cooperation by staff participating in surgeries in reducing delay time, and the hospital management's will to improve delay.

• Korea Journal of Hospital Management
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• v.23 no.3
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• pp.28-38
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• 2018

Purpose: To evaluate the medical expenditures for prostate cancer patients, including out-of-pocket costs, and compared the costs between androgen deprivation therapy and radical prostatectomy treatment. Methodology: This study combined clinical data from 357 prostate cancer patients from the Smart Prostate Cancer Database and the medical expenditure data from the claims and cost databases. We used the independent two-sample t-tests to compare androgen deprivation therapy and radical prostatectomy. Multivariable logistic regression analysis was conducted to identify determining factors for androgen deprivation therapy and radical prostatectomy treatments. Findings: The medical costs of androgen deprivation therapy treatment were much lower than radical prostatectomy treatment at the one year and remained lower until the fourth-year. However, after four years, the accumulated medical expenditures of androgen deprivation therapy become significantly higher than radical prostatectomy treatment. Patients with a higher cancer stage and older age had higher chances of being treated using androgen deprivation therapy treatment than radical prostatectomy treatment. Practical Implications: Our results show that early detection of cancer reduces the treatment cost for both patients and insurance payers. It also demonstrates that cost comparisons should be conducted over long periods of time in order to most accurately assess the costs.

• Journal of dental hygiene science
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• v.13 no.1
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• pp.29-35
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• 2013

The aim of this study was to analyze the factors that were associated to the approval for the community water fluoridation program. The subjects of this study were 751 mothers of elementary school student from September 1 to 30, 2012. Data were analyzed with chi-square, and multiple logistic regression analysis SPSS 12.0. Respondents approving and opposing for the implementation of water fluoridation program were 50.3% and 10.4%. The most associated factors of approval for the community water fluoridation program was recognition of water fluoridation program (odds ratio [OR], 2.98; 95% confidence interval [CI], 1.14~7.76), which was followed by length of residence (OR, 2.49; 95% CI, 1.39~4.47), and recognition of sealant (OR, 1.88; 95% CI, 1.02~3.50) in the order. And the approval opinion had relationship with district of residence. It is recommend that construction of public service system on education and information in order to most peoples can confidence the necessity of community water fluoridation program.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.160-167
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• 2021

This study was conducted to empirically analyze the prevalence and risk of chronic diseases in the family history of chronic diseases using data from the Korea National Health and Nutrition Examination Survey 2019. Based on 5,691 people, after controlling for socio-demographic variables that were related to family history of chronic diseases, logistic regression analysis was performed to verify the odds ratio, which was the risk of family history of chronic diseases for the prevalence of chronic diseases. The main results were founded that Odds ratio, which was the risk of chronic diseases in groups with a family history compared to those without a family history, was statistically significant at hypertension(2.623), dyslipidemia(1.868), diabetes(1.964), and arthritis(1.435) when gender, age, income status, education level and residence were controlled. These results suggest that it is not only necessary to develop a standardization tool for family history tests, but also a health and disease management system for members with a family history in terms of preventive medicine in health care. This study is significant in that it proposed a practical plan in terms of health care by controlling variables that affect the prevalence of chronic diseases and empirically identifying the risk of family history of chronic diseases.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.1
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• pp.66-74
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• 2005

This survey was conducted to assess the consumer perceptions of food-related hazard in 500 housewives from all over Korea. The subjects were selected by stratified random sampling method. The survey was performed using structured questionnaire through telephone interview by skilled interviewers. The results showed that 34.6% of the respondents felt secure and were not concerned about food safety, and 65.4% were concerned about food safety. Logistic regression analysis showed that the increasing concern on food brands, food additives (such as food preservatives and artificial color), and imported foodstuffs indicated the current increasing concern on food safety. Other related factors indicating the increasing concern on food safety were education level and care for children's health. The respondents who cared about food safety expressed a high degree of concern on processed foodstuffs such as commercial boxed lunch (93.3%), imported foods (92.7%), fastfoods (89.9%), processed meat products (88.7%), dining out (85.6%), cannery and frozen foods (83.5%), and instant foods (82.0%). The lowest degree of concern was on rice. All the respondents perceived that residues of chemical substances such as pesticides and food additives, and endocrine disrupters were the most potential food risk factors, followed by food-borne pathogens, and GMOs (Genetically Modified Organisms). However, these results were not consistent with scientific judgment. Therefore, more education and information were needed for consumers' awareness of facts and myths about food safety. In addition, the results showed that consumers put lower trust in food products information such as food labels, cultivation methods (organic or not), quality labels, and the place of origin. Nevertheless, the respondents expressed their desire to overcome alienation, and recognized the importance of knowing of the origin or the producers of food. They identified that people who need to take extreme precautions on food contamination were the producers, government officials, food companies, consumers, the consumer's association, and marketers, arranged in the order of highest to lowest. They also believed that the production stage of agriculture was the most important step for improving the level of food safety Therefore, the results indicated that there is a need to introduce safety systems in the production of agricultural products, as follows: Good Agricultural Practice (GAP), Hazard Analysis and Critical Control Point (HACCP), and Traceability System (75).

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.