• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.99-110
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• 2011

Mobile environments rapidly changing and digital convergence widely employed, mobile devices including smart phones have been playing a critical role that changes users' lifestyle in the areas of entertainments, businesses and information services. The various services using mobile devices are developing to meet the personal needs of users in the mobile environments. Especially, an LBS (Location-Based Service) is combined with other services and contents such as augmented reality, mobile SNS (Social Network Service), games, and searching, which can provide convenient and useful services to mobile users. In this paper, we design and implement the prototype of mobile personal assistant (PA) agents. Our personal assistant agent helps users do some tasks by hiding the complexity of difficult tasks, performing tasks on behalf of the users, and reflecting the preferences of users. To identify user's preferences and provide personalized services, clustering and classification algorithms of data mining are applied. The clusters of the log data using clustering algorithms are made by measuring the dissimilarity between two objects based on usage patterns. The classification algorithms produce user profiles within each cluster, which make it possible for PA agents to provide users with personalized services and contents. In the experiment, we measured the classification accuracy of user model clustered using clustering algorithms. It turned out that the classification accuracy using our method was increased by 17.42%, compared with that using other clustering algorithms.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.163-167
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• 2003

This paper investigates the lifestyles in Korean market. We will classify the groups using cluster analysis, and exploit the characteristics of innovators. These groups can be verified by multiple comparisons. This research is accomplished by sample survey between June 92003 and June 27 2003. Korean market for innovation can be classified into four groups such as innovators, early adopters, late majority, and laggards, which are similar to Rodger's classification. The ratios of four groups are 11%, 24.4%, 48.9%, 15.7% respectively. innovators, and late majority are heavy groups in that early adopter group is omitted on the contrary. Whereas innovators have a tendency to adopt the innovation quickly, rest groups have resistance for innovations and adopt slowly. The brief demographic characteristics of innovators are that the ratio of students is 44.44%, the ratio of single is 69.44%, the age between 15 and 25 is 56.95%, and the salary is relatively low compared with other cluster. The summary of lifestyle of innovators is that they are active, want to do worldwide business, want to have good relationship with high society, want to know the information of innovations, etc.

• Journal of Korean Biological Nursing Science
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• v.23 no.3
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• pp.170-179
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Diabetes mellitus and mobile applications' field of research for better understanding research trends in the past 20 years. Methods: This study was a text-mining and topic modeling study including four steps such as 'collecting abstracts', 'extracting and cleaning semantic morphemes', 'building a co-occurrence matrix', and 'analyzing network features and clustering topic groups'. Results: A total of 789 papers published between 2002 and 2021 were found in databases (Springer). Among them, 435 words were extracted from 118 articles selected according to the conditions: 'analyzed by text network analysis and topic modeling'. The core keywords were 'self-management', 'intervention', 'health', 'support', 'technique' and 'system'. Through the topic modeling analysis, four themes were derived: 'intervention', 'blood glucose level control', 'self-management' and 'mobile health'. The main topic of this study was 'self-management'. Conclusion: While more recent work has investigated mobile applications, the highest feature was related to self-management in the diabetes care and prevention. Nursing interventions utilizing mobile application are expected to not only effective and powerful glycemic control and self-management tools, but can be also used for patient-driven lifestyle modification.

• Journal of Convergence Society for SMB
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• v.6 no.1
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• pp.33-38
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• 2016

Disease of patient who visited the hospital can cause different symptoms of the disease, depending on the environment and lifestyle. Recent medical services offered in patients has changed in the environment that can be selected for treatment by analyzing the patient according to the disease symptoms. In this paper, we propose an efficient method to manage disease control because the treatment method may change at any patients suffering from the disease according to the patient conditions by grouping the different treatments to patients for disease information. The proposed scheme has a feature that can be ingested by the patient big disease information, as well as to improve the treatment efficiency of the medical treatment the increase patient satisfaction. The proposed sheme can handle big data by clustering of disease information for patients suffering from diseases such as patient consent small groups. In addition, the proposed scheme has the advantage that can be conveniently accessed via a particular keyword, the treatment method according to patient disease information. The experimental results, the proposed method has been improved by 23% in terms of efficiency compared to conventional techniques, disease management time is gained 11.3% improved results. Medical service user satisfaction seen from the survey is to obtain a high 31.5% results.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7897-7907
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• 2014

Background: There is paucity of studies defining the prevalence of non-communicable disease (NCD) risk factors in Saudi Arabia despite the surging epidemic of obesity, change in dietary habits and sedentary lifestyle. Objectives: This cross-sectional study aimed to assess the prevalence of NCDs risk factors among employees at King Faisal University in Al Hassa, Saudi Arabia and to determine the possible correlates for clustering of NCDs risk factors among them. Materials and Methods: All employees were invited to participate; the World Health Organization STEPwise approach was used for data collection which consisted of a personal interview to collect socio-demographic characteristics, NCD history, tobacco use, vegetables and fruit consumption, and physical activity (PA), followed by anthropometric measurements namely weight, height and waist circumference and blood pressure measurements, subjects were finally subjected to biochemical tests with determination of fasting plasma glucose, serum triglycerides, cholesterol and high density lipoproteins. Results: Of the surveyed employees (n=691), daily current smokers accounted for 22.7%. 94.9%, 95.1% and 86% consumed < 5 servings per day of vegetables, fruits and both fruits and vegetables respectively, 73% were physically inactive, 64% were overweight or obese, 22.1% had hypertension, and 21.5% were diabetics. Elevated cholesterol levels were found in 36.6%, low high density lipoproteins in 36.8%, and elevated triglycerides in 36.1%. Only 3% had no NCD risk factors, and 57.6% had ${\geq}3$ factors. Multivariate logistic regression showed that gender (being male, adjusted odds ratio 'aOR'=1.51), aged ${\geq}50$ years (aOR=3.06), < college education (aOR=1.75), current smokers (aOR=2.37), being obese (aOR=6.96) and having a low PA level (aOR=4.59) were the significant positive predictors for clustering of NCD risk factors. Conclusions: Over fifty percent of the studied university's employees had multiple (${\geq}3$) NCD risk factors. Screening and health promotion initiatives should be launched at least targeting the modifiable factors to avert the excessive risk for cardiovascular disease, diabetes mellitus and several types of cancers.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6005-6010
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• 2012

Background: Nasopharyngeal carcinoma (NPC) is endemic in Southern Chinese and Southeast Asian populations. Geographical and ethnic clustering of the cancer is due to genetic, environmental, and lifestyle risk factors. This case-control study aimed to identify or confirm both genetic and non-genetic risk factors for NPC in one of the endemic countries, Malaysia. Materials and Methods: A panel of 768 single-nucleotide polymorphisms (SNPs) previously associated with various cancers and known non-genetic risk factors for NPC were selected and analyzed for their associations with NPC in a case-control study. Results: Statistical analysis identified 40 SNPs associated with NPC risk in our population, including 5 documented previously by genome-wide association studies (GWAS) and other case-control studies; the associations of the remaining 35 SNPs with NPC were novel. In addition, consistent with previous studies, exposure to occupational hazards, overconsumption of salt-cured foods, red meat, as well as low intake of fruits and vegetables were also associated with NPC risk. Conclusions: In short, this study confirmed and/or identified genetic, environmental and dietary risk factors associated with NPC susceptibility in a Southeast Asian population.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5317-5325
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• 2012

The purpose of this study was to estimate the prevalence of metabolic syndrome and determine the distribution of the clustering of the metabolic risk factors, and we wanted to evaluated the related factors in urban areas. 1,388 adults of 30 years and over, not recognized as taking medicines for or having cardiovascular diseases, who underwent health package check-up at the health promotion center of a university hospital. All subjects were measured by height, weight, waist circumference, blood pressure and blood chemistry(lipid profile). As a results, the prevalence rates of metabolic syndrome of study subjects were 21.7%, and the rates of metabolic risk factors were HDL-C, blood pressure, TG, abdominal obesity and FBS in that order. And the factors such influencing on metabolic syndrome as age, BMI, smoking habits, vegetable consumption and family history of the diabetes. Consequently, it is suggested that the evaluation and intervention for lifestyle factors may be needed in order to the risk management of metabolic syndrome.

• YAKHAK HOEJI
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• v.53 no.1
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• pp.19-24
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• 2009

Metabolic syndrome, defined as the clustering of several metabolic disorders including obesity (waist circumference ${\geq}90$ if male or ${\geq}80$ if female, cm), dyslipidemia ($TG{\geq}150$ or HDL-C<40 if male or <50 if female, mg/dl), hypertension ($BP{\geq}130/85mmHg$) and hyperglycemia (fasting plasma $glucose{\geq}110mg/dl$), increases the cardiovascular risk of the general population. Recently, risk of this syndrome arises in young adults world widely. Therefore, we randomly selected and evaluated the risk of metabolic syndrome of total 43 people (group I-22, group II-21) for 2 years. Group I was 22 peoples (15 males, 7 females) with age of 22 thru 35 year old (average 28 year old) and group II was 21 people (19 male, 2 female) with age of 22 thur 32 years old (average 24 year old) in Cheongju area from March 1st thru 30th of 2008 in Cheongju area from September 1st thru 30th of 2007 in order to find out how serious this phenomenon is in young adult of Korea. 13.95% (n=7) of total people has a metabolic syndrome by NCEP/ATPIII definition among this group (group I-6, group II-1). Those of 6 have 3 or over risk factor for metabolic syndrome such as obesity, hypertension, fasting blood glucose and hypetriglyceridemia at the same time (group I-5, group II-1). Group I have more risk factor because of more higher age than group II. Therefore we need aggressively to monitor and provide them for early diagnosis, educational programs and assistance for lifestyle changes in order to prevent metabolic syndrome among young adults.