• 한국인공지능학회지
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• 제9권1호
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• pp.21-27
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• 2021

In this Paper, Since the 1990s, Korea's credit card industry has steadily developed. As a result, various problems have arisen, such as careless customer information management and loans to low-credit customers. This, in turn, had a high delinquency rate across the card industry and a negative impact on the economy. Therefore, in this paper, based on Azure, we analyze and predict the delinquency and delinquency periods of credit loans according to gender, own car, property, number of children, education level, marital status, and employment status through linear regression analysis and enhanced decision tree algorithm. These predictions can consequently reduce the likelihood of reckless credit lending and issuance of credit cards, reducing the number of bad creditors and reducing the risk of banks. In addition, after classifying and dividing the customer base based on the predicted result, it can be used as a basis for reducing the risk of credit loans by developing a credit product suitable for each customer. The predicted result through Azure showed that when predicting with Linear Regression and Boosted Decision Tree algorithm, the Boosted Decision Tree algorithm made more accurate prediction. In addition, we intend to increase the accuracy of the analysis by assigning a number to each data in the future and predicting again.

• Nutrition Research and Practice
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• 제6권1호
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• pp.51-60
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• 2012

Weight-controlling can be supported by a proper prescription of energy intake. The individual energy requirement is usually determined through resting energy expenditure (REE) and physical activity. Because REE contributes to 60-70% of daily energy expenditure, the assessment of REE is very important. REE is often predicted using various equations, which are usually based on the body weight, height, age, gender, and so on. The aim of this study is to validate the published predictive equations for resting energy expenditure in 76 normal weight and 52 obese Korean children and adolescents in the 7-18 years old age group. The open-circuit indirect calorimetry using a ventilated hood system was used to measure REE. Sixteen REE predictive equations were included, which were based on weight and/or height of children and adolescents, or which were commonly used in clinical settings despite its use based on adults. The accuracy of the equations was evaluated on bias, RMSPE, and percentage of accurate prediction. The means of age and height were not significantly different among the groups. Weight and BMI were significantly higher in obese group (64.0 kg, $25.9kg/m^2$) than in the non-obese group (44.8 kg, $19.0kg/m^2$). For the obese group, the Molnar, Mifflin, Liu, and Harris-Benedict equations provided the accurate predictions of > 70% (87%, 79% 77%, and 73%, respectively). On the other hand, for non-obese group, only the Molnar equation had a high level of accuracy (bias of 0.6%, RMSPE of 90.4 kcal/d, and accurate prediction of 72%). The accurate prediction of the Schofield (W/WH), WHO (W/WH), and Henry (W/WH) equations was less than 60% for all groups. Our results showed that the Molnar equation appears to be the most accurate and precise for both the non-obese and the obese groups. This equation might be useful for clinical professionals when calculating energy needs in Korean children and adolescents.

• 동의신경정신과학회지
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• 제22권3호
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• pp.13-22
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• 2011

Objectives : This study was to research the relationship between Hwa-Byung and Framingham coronary risk score(FRS), cardiovascular disease. Methods : 649 people participated in the community based cohort study in Wonju City of South Korea from July 2nd to August 30th in 2006. Educated investigators checked up systolic & diastolic blood pressure and surveyed Hwa-Byung Diagnostic Interview Schedule(HBDIS), cohort questionnaire about gender, age, smoking, diabetes. Blood sample was collected from participants to analyze total cholesterol, HDL-cholesterol. FRS was calculated from collected data. 10-year prediction of coronary heart disease was determined from FRS by using score sheet that is estimated by Wilson et al. Collected data were analyzed by the chi-square test. Results : 1. Low risk number of people was 18(52.9%) in Hwa-Byung group, 263(42.8%) in non Hwa-Byung group. p-value was 0.472. Difference of the two group was invalid. 2. The number of people below or equal to average 10-year prediction of coronary heart disease as gnder & age, Hwa-Byung group was 19(55.9%), non Hwa-Byung group was 412(67.0%). p-value was 0.251. Difference of the two group was invalid. Conclusions : There was no correlationship Between Hwa-Byung and 10-year prediction of coronary heart disease.

• Physical Therapy Rehabilitation Science
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• 제11권3호
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• pp.285-295
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• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• 융합보안논문지
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• 제23권1호
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• pp.89-95
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• 2023

자동차보험 사고 환자의 진료비 감소를 위한 대책 마련에 도움을 주기 위해 본 연구에서는 자동차보험 사고 40대~50대 경상 환자들의 진료비에 가장 핵심 요소인 진료 기간을 예측하고 진료 기간에 영향을 미치는 요인을 분석하였다. 이를 위해 Decision Tree 등 5개 알고리즘을 활용한 머신러닝 모델을 생성하고 모델간에 그 성능을 비교·분석하였다. 진료 기간 예측에 정밀도, 재현율, FI 점수 등 3가지 평가 지표에서 좋은 성능을 나타낸 알고리즘은 Decision Tree, Gradient Boosting 및 XGBoost 등 3가지였다. 그리고 진료 기간 예측에 영향을 미치는 요인 분석 결과, 병원의 종류, 진료 지역, 나이, 성별 등으로 나타났다. 본 연구를 통해 AutoML을 활용한 손쉬운 연구 방법을 제시하였으며, 본 연구 결과가 자동차보험 사고 진료비 경감을 위한 정책에 도움이 되기를 기대한다.

• 말소리와 음성과학
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• 제11권3호
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• pp.49-55
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• 2019

본 연구에서는 한국인 정상 성인에서 음성(VRP) 및 말소리 범위 프로파일(SRP)을 이용하여 문단 읽기 시 전기성문파형검사(EGG)를 이용하여 측정한 평균 발화 기본주파수(SFF)를 예측할 수 있는지 알아보고자 하였다. 또한 추정된 기본주파수(ESFF)와 실제 SFF 간 차이(DSFF)에 있어 성별 차이가 있는지 알아보고자 하였다. 연구대상은 정상 음성을 가진 한국어 모국어 화자 85명이었다. 각 대상자는 /a/ 발성으로 전체 음역대를 측정하는 VRP 과제, '가을' 문단의 첫 번째 문장을 읽어 말소리 산출 시 음역대를 측정하는 SRP 과제, 전체 문단을 읽어 SFF를 측정하는 문단 읽기 과제를 수행하였다. VRP와 SRP를 통해 측정된 음역대 관련 변수들와 연령, 성별이 EGG를 통해 측정된 SFF를 예측할 수 있는지 알아보기 위해 단계적 다중회귀분석을 시행하였고, 예측된 ESFF와 SFF 간 차이의 절대값(DSFF)과 그 합계를 구하였다. 연구 결과, SFF의 예측변인은 VRP에서는 최저음도, 음도범위, 성별, 연령(adjusted $R^2=.931$)이었으며, SRP에서는 반음 단위 음역대와 최고음도(adjusted $R^2=.963$)였다. VRP와 SRP를 통해 예측된 두 가지 ESFF와 실제 SFF 사이에는 강한 양의 상관관계가 있었다. VRP와 SRP를 이용한 DSFF와 그 합계에 있어 성별 차이는 없었다. 결론적으로 VRP와 SRP를 통해 문단 읽기 시 SFF를 예측할 수 있었으며, SFF의 이상을 보일 수 있는 음성장애 환자에서 후속 연구를 통하여 임상적 시사점을 탐색할 필요가 있을 것으로 여겨진다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.99-100
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• 2008

This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(B)
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• pp.303-305
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• 2012

사람들은 주변의 영향을 받아 가면서 각자의 독특한 글쓰기 양식을 만들어간다. 따라서 같은 연령대와 성별을 가지는 사람들은 유사한 글쓰기 양식을 나타내는 경향이 있다. 이와 같은 가정을 바탕으로, 본 연구에서는 다양한 연령대와 성별의 사람들이 작성한 트윗의 문체를 분석하여 임의의 트윗을 작성한 저자의 연령대와 성별을 예측하는 실험을 진행하였다. 한국어 웹 언어에서 자주 보이는 표현들을 토대로 구성한 자질들과, 그에 비해 데이터와 관계가 적은 n-gram 단위의 자질들을 함께 사용하여 예측을 진행함으로써, 최대 공산 기준치보다 25%가량 높은 정확도를 보이는 예측 결과를 얻게 되었다. 이와 함께 각 자질 구성이 예측에 얼마나 효율적으로 기여하는지에 대한 이해도를 높일 수 있었다.

• 아동학회지
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• 제17권2호
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• pp.181-193
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• 1996

The purpose of this study was: (1) to examine relationships between social development and day care quality, child-care experience and family characteristics, and (2) to investigate the explainability of those related variables for social development. Subjects for this study were 252 4-year-old children and their mothers from 32 day care centers in Seoul. Harms & Clifford's Early Childhood Environment Rating Scale was used to measure the quality of day care. The main results were as follows: (1) Day care quality, child-care experience and family characteristics were significantly related to social development. (2) Child's gender, months of age, mother's child rearing attitude, the length of child-care experience, overall quality of day care, and group size significantly predicted social development. 33％ of the variance of social development was explained by these variables. The relative influence of these variables to the prediction of social development was about the same.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.230-237
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• 2022

The Community Social Service Investment project started as a state subsidy project in 2007 and has grown very rapidly in quantitative terms in a short period of time. It is a bottom-up project that discovers the welfare needs of people and plans and provides services suitable for them. The purpose of this study is to analyze using big data to determine the social response to local community service investment projects. For this, data was collected and analyzed by crawling with a specific keyword of community service investment project on Google and Naver sites. As for the analysis contents, monthly search volume, related keywords, monthly search volume, search rate by age, and gender search rate were conducted. As a result, 10 items were found as related keywords in Google, and 3 items were found in Naver. The overall results of Google and Naver sites were slightly different, but they increased and decreased at almost the same time. Therefore, it can be seen that the community service investment project continues to attract users' interest.