• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of the East Asian Society of Dietary Life
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• v.18 no.4
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• pp.524-530
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• 2008

In this study, the effects of feed containing citrus byproducts on the physicochemical characteristics and palatability of Korean native chickens were investigated. The Korean native chickens used in this study were divided into two groups: T0 (chickens that were not fed citrus byproducts until they were 39 weeks old) and T1 (chickens that were fed citrus byproducts). The feed given to the T1 chickens was the same as that given to the T0 chickens for the first 16 weeks. Between weeks $17{\sim}39$, the feed given to the T1 chickens was prepared by adding 4% of the citrus byproducts to the feed given to the T0 chickens. The chickens used in the experiment were chilled for 2 days after being sacrificed. The feed containing citrus byproducts did not cause any statistically significant differences in the breast and thigh characteristics of lightness ($L^*$ value), redness ($a^*$ value), yellowness ($b^*$ value), water-holding capacity, frozen loss, thawing loss and boiling loss. As for the rheological properties, there was no statistically meaningful difference in the breast/thigh characteristics of springiness, cohesiveness, gumminess, and chewiness between the T0 and T1 chickens. However, hardness and shear force were significantly lower in the T1 chickens than in the T0 chickens (p<0.05). The acid and peroxide values were also lower in the T1 chickens than in the T0 chickens, but the difference was not statistically significant. Antioxidant activity was better in the T1 chickens than in the T0 chickens. Thus, the results of the present study show that consumption of citrus byproducts did not affect the color and smell of raw meat. The palatability of boiled meat was significantly better in the T1 chickens than in the T0 chickens.

• Therapeutic Science for Rehabilitation
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• v.9 no.2
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• pp.99-117
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• 2020

Objective : The purpose of this study was to study the effects of occupation-based community rehabilitation on occupational performance skills and activities of daily living in stroke disabled persons living in the community, and to investigate the changes in occupation quality and satisfaction. Methods : In this single-subject ABA design study with follow-up evaluation, one severely disabled person diagnosed with stroke who lived in the community was recruited. The procedure consisted of a total of 25 sessions for 17 weeks. Intervention was according to occupation-based community rehabilitation, and the researcher visited the subject's home. Individualized intervention was applied according to the OTIPM. The intervention was composed of task assignment and feedback, home environment modification, information-related caregiver education, and community resource network. The evaluation of each session included the changes in the frequency of occupational performance skills, the quality of occupational performance in daily life, and the changes in occupational satisfaction, activities of daily living, quality of life, and maintenance of in the occupational performance skills during follow-up. The results were visually analyzed using a bar graph and a linear graph. Results : The results showed that the occupation-based community rehabilitation improved activities of daily living such as putting on socks, shoes slip-on, and upper body dressing garment within reach. Within the framework of the AMPS, it was confirmed that the quality of occupational performance was improved in all the subjects, and the degree of satisfaction also improved. Conclusion : This study showed that occupation-based rehabilitation can improve the occupational performance skills of stroke home disabled people positively affect the quality of occupational performance in daily life. Therefore, I think it is meaningful that useful for them.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Journal of Music and Human Behavior
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• v.1 no.1
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• pp.1-10
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• 2004

Being engaged in the musical tasks needs cognitive skills to perceive musical sound, organize them into meaningful unit, store them in the memory and retrieve them when needed. These skills are also required for academic tasks indicating that there is positive correlation between skills for musical and academic tasks. Based on these findings, the study purported to examine whether the developed sessions can enhance cognitive skills which is composed of auditory information skills, which is composed of perceiving sounds, organizing them into groups based on the existing information or organization pattern, and short-term memory skills. Eighteen elementary students in 4, 5, and 6th grades have participated in the study. The study has administered Music Cognitive Skills Test(MCST) before and after implementing music therapy sessions. The MCST consisted of five parts, first one measuring the rhythm imitating skills, second, measuring the melodic imitation skills, third, measuring discriminative skills in identifying higher pitch, fourth, measuring discriminative skills in identifying identical chords, and lastly, measuring the tone retention skills. The results indicated that there was statistical difference between the pre and post test in rhythm and melody imitation skills. Because reproduction of perceived rhythm patterns requires memory skills, imitating patterns are considered cognitive skills. Also melody is defined adding spatial dimension to the rhythm which is temporal concept. Being able to understand melodic pattern and to reproduce the pattern also requires cognitive skills. The subjects have shown significant improvement in these two areas. In other areas, there were definite increase of scores, however, no significant differences. The study also explores interpretation of these results and also observed consistencies among the participants in completing the musical tasks.

• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.477-486
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• 2016

We investigated the growth pattern of Red-tongued viper snakes (Gloydius ussuriensis), which were captured from the islet of the Jeju Island, Gapado between April, 2006 and November, 2009. The results indicated that there were some snakes that grew relatively fast, but most snakes either almost did not grow or grew around 10mm in snout-vent length during one year period. High growth rates was April and June. Since the growth rate of snakes is highly correlated with their foods, these results implied that the feeding activity of Red-tongued viper snakes is high during this period compared to other months. In female, difference in body condition between good-conditioned and bad-conditioned snakes became large as time elapsed from April to June. The body condition of the male Red-tongued viper snakes improved with the progression of time from April till June. Many of the Red-tongued viper snakes were captured between April and June, while they were rarely captured between July and September. Some of the Red-tongued viper snakes were captured during the autumn season. This tendency was because snakes were rarely active during hibernation and peak summer seasons. Thus, Red-tongued viper snakes are active between April and June and between September and November. They then go into hibernation as the temperature dropped in November. Furthermore, the limitation of the movement period of the Red-tongued viper snakes restricted their feeding activities while foods became scarce, which ultimately restricted their overall growth rate. The growth rate of the snakes decreased with age. The snout-vent length of the Red-tongued viper snakes and growth rate showed a negative correlation (r = -0.591), however, it was not statistically significant due to small sample size. The findings from this study could provide meaningful information in the further study of the life cycle of Red-tongued viper snakes.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.113-129
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• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.16 no.1
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• pp.34-41
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• 2016

Adult-onset type II citrullinemia (CTLN2) is characterized by episodes of neurologic symptoms associated with hyperammonemia leading to disorientation, irritability, seizures, and coma. CTLN2 is distinct from classical citrullinemia, which is caused by a mutation of the argininosuccinic acid synthetase (ASS) gene. The serum citrulline level is elevated, while the activity of ASS in liver tissue is decreased. CTLN2 is known to have a poor prognosis if the proper treatment is not taken. We reported a female aged 37 years who developed recurrent attacks of altered consciousness, aberrant behavior, and vomiting. We initially suspected the patient had CTLN2 because of the signs of hyperammonemic encephalopathy, such as altered mentality, memory disturbance, and aberrant behaviors provoked by exercise-induced stress and excessive intravenous amino acid administration. Through her peculiar diet preferences and laboratory findings that included hyperammonemia and citrullinemia, we diagnosed the patient as CTLN2, and SLC25A13 sequencing revealed known compound heterozygous mutations (IVS11+1G>A, c.674C> A). Her parents were heterozygous carriers, and we identified that her older sister had the same mutations. The older sister had not experienced any episodes of hyperammonemia, but she had peculiar diet preferences. The patient and her sister have been well with conservative management. When considering the clinical course of CTLN2, it was meaningful that the older sister could be diagnosed early in an asymptomatic period and that preemptive treatment was employed. Through this case, CTLN2 should be considered in adults who present symptoms of hyperammonemic encephalopathy without a definite etiology. Because of its rare incidence and similar clinical features, CTLN2 is frequently misdiagnosed as hepatic encephalopathy, and it shows a poor prognosis due to the lack of early diagnosis and proper treatment. A high-carbohydrate diet, which is usually used to treat other urea cycle defects, can also exaggerate the clinical course of CTLN2, so proper metabolic screening tests and genetic studies should be performed.

• The Korean Society of Law and Medicine
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• v.16 no.1
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• pp.155-190
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• 2015

The court sentenced meaningful decisions related to the medical service in 2014. The court assumed the negligence of medical staff in the accident if being broken while using the medical equipment for not an original purpose at the time of surgery and ruled that the compensation for damage can be recognized in recognition of the causal relationship between the explanation duty violation and side effect's happening when unproven surgery on safety is implemented regarding the duty of explanation, that in the case of cosmetic surgery, the subject on the duty of explanation needs to be expanded compared to the general medical practice and that the duty of explanation cannot be accepted for the range that cannot be expectable. Also, the court has provided the requirement and limitation of self-determination exercise in case of the crash between patient's self-determination and doctor's duty of care and has ruled that as automobile insurance contract is a contract with the insurance company to pay regarding liability for car accidents, treating patients and taking the insurance money is not illegal activity even for the unlicensed hospital violating the medical law while established. The judgment stating the opinion that medical practitioners cannot be punished according to the medical law prohibiting the receiving of rebate in case that medical practitioners did not receive benefit while the medical institution itself gained an unfair economic benefit also stands out. And the court has ruled that even if the medical institution who received a business suspension is closed, the suspension is still effective in case that the same operator opens a new medical institution in the same place, ruled on the requirement to conduct a medical service outside of the medical institution that the doctor opened and ruled that the administrative penalty cannot be conducted prior to the conviction on charge of violating the medical law.