• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.1-7
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• 2008

• Journal of Sasang Constitutional Medicine
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• v.23 no.2
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• pp.194-207
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• 2011

1. Objectives: This study is about a development of Sasang constitutional classification algorithm using facial information. 2. Methods: We analysed the datum of middle aged (20~48) women collected by multi-center researchers in 2007. And this study analysed the data of the measurement of the face by 3D-AFRA (3-Dimensional Automatic Face Recognition Apparatus) and the items of impression by SDQ. We used multiple comparison, exploratory discriminant analysis and clinical decision to select optimal 3D facial variables which will be input in discriminant analysis model. And we used univariate F values and stepwise discriminant function analysis to choose best impression variables. 3. Results and Conclusions: In this study, derived discriminant function's explanation power was 39% in female group. Diagnostic accuracy rate was 66.0% in female group. And in test sample, Sasang constitutional diagnostic accuracy rate was 56.9%. In this process we could help improve the objectification of Sasang constitution diagnosis.

• Health Policy and Management
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• v.9 no.2
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• pp.1-20
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• 1999

This study was conducted to investigate the predictors of hospital bankruptcy in Korea and to examine the predictive power for 3 types of statistical models of hospital bankruptcy. Data on 17 financial and 4 non-financial indicators of 30 bankrupt and 30 profitable hospitals in 1. 2, and 3 years before bankruptcy were obtained from the hospital performance databank of Korea Institute of Health Services Management. Significant variables were identified through mean comparison of each indicator between bankrupt and profitable hospitals, and the predictive power of statistical models of hospital bankruptcy were compared. The major findings are as follows. 1. Nine out of 21 indicators - fixed ratio, quick ratio, operating profit to total assets, operating profit to gross revenue, normal profit to total assets,normal profit to gross revenue, net profit to gross revenue, inventories turnrounds, and added value per adjusted patient - were found to be significantly predictitive variables in Logit and Probit models. 2. The predicdtive power of discriminant model of hospital bankruptcy in 1. 2, and 3 years before bankruptcy were 85.4, 79.0, and 83.8% respectively. With regard to the predictive power of the Logit model of hospital bankruptcy, they were 82.3, 75.8, and 80.6% respectively, and of the Probit model. 87.1. 80.6, and 88.7% respectively. 3. The predictive power of the Probit model of hospital bankruptcy is better than the other two predictive models.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.823-829
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• 2001

Even though a rapid development of modem medical science, paralysis disease is a highly dangerous and murderous disease. Shin et al. (1978) constructed the diagnosis expert system which identify a type of the paralysis disease from symptoms of a paralysis disease patients by using the canonical discriminant analysis. The decision tree-based analysis, however, has advantages over the method used in Shin et al. (1998), such as it does not need assumptions - linearity and normality, and suggest appropriate diagnosis procedure which is easily explained. In this paper, we applied the decision tree to construct the model which Identify a type of the paralysis disease.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.221-233
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• 2007

Which factors should influence consumer consideration to subscribe to Voice over Internet Protocol (VoIP)? Policy issues, managerial concerns, and demographic variables are possible factors. This paper discusses policy and managerial issues regarding VoIP adoption. A model that explains VoIP adoption is proposed and tested. This study analyzes a survey of 750 prospective VoIP users in Korea. The testing is accompanied by logistic regression and discriminant analysis. The results show that trust in VoIP, relative comparison of Quality to fixed service, numbering plan, satisfactions of call Quality and customer services on both fixed and mobile services have impacts on the adoption of VoIP. Implications for VoIP providers and policy makers are presented.

• The Korean Journal of Pain
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• v.32 no.2
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• pp.120-128
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• 2019

Background: We aimed to explore the American College of Rheumatology (ACR) 1990 and 2011 fibromyalgia (FM) classification criteria's items and the components of Fibromyalgia Impact Questionnaire (FIQ) to identify features best discriminating FM features. Finally, we developed a combined FM diagnostic (C-FM) model using the FM's key features. Methods: The means and frequency on tender points (TPs), ACR 2011 components and FIQ items were calculated in the FM and non-FM (osteoarthritis [OA] and non-OA) patients. Then, two-step multiple logistic regression analysis was performed to order these variables according to their maximal statistical contribution in predicting group membership. Partial correlations assessed their unique contribution, and two-group discriminant analysis provided a classification table. Using receiver operator characteristic analyses, we determined the sensitivity and specificity of the final model. Results: A total of 172 patients with FM, 75 with OA and 21 with periarthritis or regional pain syndromes were enrolled. Two steps multiple logistic regression analysis identified 8 key features of FM which accounted for 64.8% of variance associated with FM group membership: lateral epicondyle TP with variance percentages (36.9%), neck pain (14.5%), fatigue (4.7%), insomnia (3%), upper back pain (2.2%), shoulder pain (1.5%), gluteal TP (1.2%), and FIQ fatigue (0.9%). The C-FM model demonstrated a 91.4% correct classification rate, 91.9% for sensitivity and 91.7% for specificity. Conclusions: The C-FM model can accurately detect FM patients among other pain disorders. Re-inclusion of TPs along with saving of FM main symptoms in the C-FM model is a unique feature of this model.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• Journal of Korean Academy of Nursing
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• v.46 no.1
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• pp.69-78
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• 2016

Purpose: The purpose of this study was to develop a wellness index for workers (WIW) and examine the validity and reliability of the WIW for assessing workers' wellness. Methods: The developmental process for the instrument included construction of a conceptual framework based on a wellness model, generation of initial items, verification of content validity, preliminary study, extraction of final items, and psychometric testing. Content validity was verified by 4 experts from occupational health nursing and wellness disciplines. The construct validity, convergent validity and discriminant validity were examined with confirmatory factor analysis. The reliability was examined with Cronbach's alpha. The participants were 494 workers from two workplaces. Results: Eighteen items were selected for the final scale, and the results of the confirmatory factor analysis supported a five-factor model of wellness with acceptable model fit, and factors named as physical emotional social intellectual occupational wellness. The convergent and discriminant validity were also supported. The Cronbach's alpha coefficient was .91. Conclusion: The results indicate that the WIW is a valid and reliable instrument to comprehensively assess workers' wellness, and to provide basic directions for developing workplace wellness program.

• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.145-151
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• 2011

In view of the hypothesis that the effects of Parkinson's disease on voice production can be detected before pharmacological intervention, the prosodic features of patients with idiopathic Parkinson's disease (IPD) and a healthy aging group were diagnostically analyzed with the long term object of establishing, for clinical purposes, early disease-progression biomarkers. Twenty patients (male 8; female 12) with IPD (prior to pharmacological intervention) and a healthy control group of 22 (male 10; female 12) were selected. Ten sentences were recorded with a head-worn microphone. One sentence was chosen for the analysis of this paper. Relevant parameters, i.e. 3-dimensional model (F0, intensity, duration) and pitch and intensity related slopes (maxEnergy, maxF0, meanAbS, semiT, meanEnergy, meanF0), were analyzed by two-group discriminant analysis. The stepwise estimation method of discriminant analysis was performed by gender. The discriminant functions predicted 83.9% of the male test data correctly while the prediction rate was 93.1% for the female group. The results showed that meanF0_slope and semiT_slope were more important parameters than the others for the male group. For the female group, the meanEnergy_slope and maxEnergy_slope were the important ones. These findings indicate that significant parameters are different for the male and female group. Gender lifestyle may be responsible for this difference. Dysprosodic features of IPD show not simultaneously but progressively in terms of F0, intensity and duration.

• Journal of Ginseng Research
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• v.37 no.3
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• pp.341-348
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• 2013

Identification of the origins of Panax ginseng has been issued in Korea scientifically and economically. We describe a metabolomics approach used for discrimination and prediction of ginseng roots from different origins in Korea. The fresh ginseng roots from six ginseng cooperative associations (Gangwon, Gaeseong, Punggi, Chungbuk, Jeonbuk, and Anseong) were analyzed by UPLC-MS-based approach combined with orthogonal projections to latent structure-discriminant analysis multivariate analysis. The ginsengs from Gangwon and Gaeseong were easily differentiated. We further analyzed the metabolomics results in subgroups. Punggi, Chungbuk, Jeonbuk, and Anseong ginseng could be easily differentiated by the first two orthogonal components. As a validation of the discrimination model, we performed blind prediction tests of sample origins using an external test set. Our model predicted their geographical origins as 99.7% probability. The robust discriminatory power and statistical validity of our method suggest its general applicability for determining the origins of P. ginseng samples.