• Asian Pacific Journal of Cancer Prevention
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• 제13권4호
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• pp.1267-1272
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• 2012

Background and Objective: The optimal resection extent for clinically unilateral papillary thyroid microcarcinoma (PTMC) remains controversial. The objective was to investigate risk factors associated with occult contralateral carcinoma, and put emphasis on the predictive value of preoperative BRAF mutation. Materials and Methods: 100 clinically unilateral PTMC patients all newly diagnosed, previously untreated were analyzed in a prospective cohort study. We assessed the T1799A BRAF mutation status in FNAB specimens obtained from all PTMC patients before undergoing total thyroidectomy (TT) and central lymph node dissection (CLND) for PTMC. Univariate and multivariate analyses were used to reveal the incidence of contralateral occult cancer, difference of risk factors and predictive value, with respect to the following variables: preoperative BRAF mutation status, age, gender, tumor size, multifocality of primary tumor, capsular invasion, presence of Hashimoto thyroiditis and central lymph node metastasis. Results: 20 of 100 patients (20%) had occult contralateral lobe carcinoma. On multi-variate analysis, preoperative BRAF mutation (p = 0.030, OR = 3.439) and multifocality of the primary tumor (p = 0.004, OR = 9.570) were independent predictive factors for occult contralateral PTMC presence. However, there were no significant differences between the presence of occult contralateral carcinomas and age, gender, tumor size, capsular invasion, Hashimoto thyroiditis and central lymph node metastasis. Conclusions: Total thyroidectomy, including the contralateral lobe, should be considered for the treatment of unilateral PTMC if preoperative BRAF mutation is positive and/or if the observed lesion presents as a multifocal tumor in the unilateral lobe.

• Asian Pacific Journal of Cancer Prevention
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• 제15권2호
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• pp.663-669
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• 2014

Background: With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and Methods: A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results: The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions: The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.

• 전기학회논문지
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• 제66권11호
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• pp.1628-1633
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• 2017

The purpose of this study was to identify the difference and correlation in elbow joint maximal flexion strength according to measurement methods and characteristics of muscular contraction, and to develop the predictive equation of elbow joint maximal flexion strength for the optimal exercise intensity setting and accurate measurement. Subjects were 30 male university students. Elbow joint maximal flexion strength of isokinetic contraction, isometric contraction at $75^{\circ}$ elbow joint flexion position, isotonic concentric 1RM, manual muscle strength (MMT) were measured with isokinetic dynamometer, dumbbell, and manual muscle tester. Pearson's r, linear regression equation, and multiple regression equation between variables were calculated. As a result, the highest value was isometric contraction. The second highest value was MMT. The third highest value was isokinetic contraction. 1RM was the lowest. Predictive equations of elbow joint maximal flexion strength between isometric and isokinetic contraction, between isometric contraction and 1RM, among isometric contraction, 1RM, and body weight were developed. In conclusion, 1RM and isokinetic elbow joint maximal flexion strength could be seemed to underestimate the practical elbow joint maximal flexion strength. And it is suggested that the developed predictive equations in this study should be useful in criteria- and goal-setting for resistant exercise and sports rehabilitation after elbow joint injury.

• 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.

• 대한통합의학회지
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• 제10권3호
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• pp.107-117
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• 2022

Purpose : The purpose of this study was to establish a model of the predictive factors for success or failure of examinees undertaking the Korean physical therapist licensing examination (KPTLE). Additionally, we assessed the pass/fail cut-off point. Methods : We analyzed the results of 10,881 examinees who undertook the KPTLE, using data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was the test result (pass or fail), and the input variables were: sex, age, test subject, and total score. Frequency analysis, chi-square test, descriptive statistics, independent t-test, correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analyses were performed on the data. Results : Sex and age were not significant predictors of attaining a pass (p>.05). The test subjects with the highest probability of passing were, in order, medical regulation (MR) (Odds ratio (OR)=2.91, p<.001), foundations of physical therapy (FPT) (OR=2.86, p<.001), diagnosis and evaluation for physical therapy (DEPT) (OR=2.74, p<.001), physical therapy intervention (PTI) (OR=2.66, p<.001), and practical examination (PE) (OR=1.24, p<.001). The cut-off points for each subject were: FPT, 32.50; DEPT, 29.50; PTI, 44.50; MR, 14.50; and PE, 50.50. The total score (TS) was 164.50. The sensitivity, specificity, and the classification accuracy of the prediction model was 99 %, 98 %, and 99 %, respectively, indicating high accuracy. Area under the curve (AUC) values for each subject were: FPT, .958; DEPT, .968; PTI, .984; MR, .885; PE, .962; and TS, .998, indicating a high degree of fit. Conclusion : In our study, the predictive factors for passing KPTLE were identified, and the optimal cut-off point was calculated for each subject. Logistic regression was adequate to explain the predictive model. These results will provide universities and examinees with useful information for predicting their success or failure in the KPTLE.

• 한방재활의학과학회지
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• 제24권4호
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• pp.155-162
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• 2014

Objectives Advanced researches on the relationship between obesity and heart rate variability (HRV), heretofore, focused on characteristics of HRV depending on the state of obesity. However, the previous researches have not quantified predictive power of HRV toward the obesity-related variables, which is rather more meaningful for clinicians who regularly treat obese patients. Hence, we designed a research to investigate whether HRV could predict serum levels of obesity-related metabolites. Methods Ninety obese premenopausal women meeting the inclusion criteria were recruited. The HRV test, blood sampling, and measurement of physical traits were conducted. Multiple regression analysis of the measurement data was carried out, putting obesity-related metabolites (insulin, glucose, triglyceride, hs-CRP, HDL, LDL, total cholesterol) as outcome variables and the others as predictors. To select appropriate predictive variables, the Akaike's Information Criterion (AIC) was applied. Normality and homoskedasticity of residuals for each model were tested to identify if there were any violations of the regression analysis's basic assumption. Logarithm transformation was used for the values of the concentration of metabolites and the HRV. Results The regression model including Total Power (TP) value and BMI had significant predictive power for serum insulin concentration (F(2, 88)=835.7, p<0.001, $R^2=0.95$). The regression coefficient of ln (TP) was -0.1002. However, it was not sure if the HRV could predict concentrations of other metabolites. Conclusions The results suggest that the Total Power (TP) value of the HRV can predict the level of serum insulin. If the BMI could be assumed as being constant, when the TP value is multiplied by n, the predicted change of insulin could be drawn by multiplying $n^{-0.1002}$. The uncertainty of this model can be assumed as approximately 5%.

• 벤처창업연구
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• 제16권3호
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• pp.73-86
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• 2021

이 연구의 목적은 기업가적 혁신성을 정확하게 예측하는 고도화된 분석 모델을 탐색하는 것이다. 기업가정신 연구 분야에서는 최초로, 데이터 과학적 접근방식에 해당되는 기계학습(Machine learning)을 이용해 기업가적 혁신성(entrepreneurial innovativeness)을 예측하는 모델을 제시한다. 예측모델을 구축하기 위하여 Global Entrepreneurship Monitor(GEM)의 62개국 22,099건 데이터를 이용한다. 27개 설명변수로 이뤄진 데이터 셋을 토대로 전통적 통계방법인 다중회귀분석과, 회귀트리, 랜덤포레스트, XG부스트, 인공신경망 등 기계학습을 이용한 예측모델을 구축하고 각 모델의 성능을 비교한다. 모델의 성능 평가를 위해 RMSE(Root mean square error), MAE(Mean absolute error)와 상관관계(Correlation) 등 지표를 사용한다. 분석 결과 5가지 기계학습 기반 모델은 모두 전통적 방법에 비해 우수한 성능을 보였으며, 예측 성능이 가장 좋은 모델은 XG부스트였다. XG부스트를 통한 기업가적 혁신성 예측에 있어서 기여도가 높은 변수는 창업가의 기회인지 및 시장 확장의 교차항 변수이며, 이는 신시장에서 기회를 획득하고자 하는 유형의 창업기업이 높은 혁신성을 보인다는 점을 확인했다. 이 연구는 고도화된 분석방법인 기계학습을 이용해 새로운 예측모델을 제시, 기업가정신 연구의 시야를 확장했다는 점에서 의의를 지닌다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권11호
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• pp.541-546
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• 2021

최근 전 세계적으로 인공지능과 머신러닝을 기반으로 임상정보를 활용한 다양한 고혈압 식별 및 예측 모델이 개발되고 있다. 그러나 고혈압 관련 모델에 대한 대부분의 선행연구는 침습적 및 고가의 분석비용을 통한 변수들이 대부분 사용되었고, 인종과 국가의 특징에 대한 고려가 충분히 제시되지 않았다. 따라서 이 연구의 목적은 일반적인 사회인구 통계학적 변수만을 사용하여 쉽게 이해할 수 있는 한국인 성인 고혈압 예측 모델을 제시하는 것이다. 이 연구에서 사용된 데이터는 질병관리청 국민건강영양조사 (2018년)를 이용하였다. 남성에서, wrapper-based feature subset selection 메소드와 naive Bayes를 이용한 모델이 가장 높은 예측 성능 (ROC = 0.790, kappa = 0.396)을 보였다. 여성의 경우, correlation-based feature subset selection 메소드와 naive Bayes를 사용한 모델이 가장 높은 예측 성능(ROC = 0.850, kappa = 0.495)을 나타내었다. 또한 모든 모델들에서 사회인구 통계학적 변수들만을 이용한 고혈압의 예측 성능이 남성보다 여성에게서 더 높게 나타나는 것을 발견하였다. 본 연구의 결과인 machine learning 기반 고혈압 예측 모델은 한국인에 대한 단순한 사회인구학적 특성만을 사용하였기 때문에 향후 공중 보건 및 역학 분야에서 쉽게 사용될 수 있을 것으로 예상된다.

• 제어로봇시스템학회논문지
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• 제10권3호
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• pp.280-285
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• 2004

This paper is concerned with the modeling and identification of sequencing batch reactor (SBR) via genetic algorithm based polynomial neural network (GA-based PNN). The model describes a biological SBR used in the wastewater treatment process fur nitrogen removal. A conventional polynomial neural network (PNN) is applied to construct a predictive model of SBR process fur nitrogen removal before. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems, we propose GA-based PNN. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. Simulation results have shown that the complex SBR process can be modeled reasonably well by the present scheme with a much simpler structure compared with the conventional PNN model.

• 대한간호학회지
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• 제42권6호
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• pp.810-818
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• 2012

Purpose: This study was done to explore factors relating to number of falls among community-dwelling elders, based on gender. Methods: Participants were 403 older community dwellers (male=206, female=197) aged 60 or above. In this study, 8 variables were identified as predictive factors that can result in an elderly person falling and as such, supports previous studies. The 8 variables were categorized as, exogenous variables; perceived health status, somatization, depression, physical performance, and cognitive state, and endogenous variables; fear of falling, ADL & IADL and frequency of falls. Results: For men, ability to perform ADL & IADL (${\beta}_{32}$=1.84, p<.001) accounted for 16% of the variance in the number of falls. For women, fear of falling (${\beta}_{31}$=0.14, p<.05) and ability to perform ADL & IADL (${\beta}_{32}$=1.01, p<.001) significantly contributed to the number of falls, accounting for 15% of the variance in the number of falls. Conclusion: The findings from this study confirm the gender-based fall prediction model as comprehensive in relation to community-dwelling elders. The fall prediction model can effectively contribute to future studies in developing fall prediction and intervention programs.