• 동의생리병리학회지
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• 제25권6호
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• pp.1113-1118
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• 2011

In this paper, when a physician make a diagnosis of the pattern identification (PI) in Korean stroke patients, the development methods of the PI classification function is considered by diagnostic questionnaire of the PI for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PI subtypes diagnosed by two physicians with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PI using Korean Stroke Syndrome Differentiation Standard was consist of the 44 items (Fire heat(19), Qi deficiency(11), Yin deficiency(7), Dampness-phlegm(7)). Using the 44 items, we took diagnostic and prediction accuracy rate through of discriminant model. The overall diagnostic and prediction accuracy rate of the PI subtypes for discriminant model was 74.37%, 70.88% respectively.

• 스마트미디어저널
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• 제11권4호
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• pp.62-68
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• 2022

수산 양식장 질병 감염의 확산을 사전에 차단을 위해서는 양식장의 수질 환경 및 생육 어류의 상태를 실시간 모니터링하면서 어류의 질병을 예측하는 시스템이 필요하다. 어류 질병 예측의 기존 연구는 이미지 처리 기법이 대부분이었으나 최근에는 딥러닝 기법을 통한 질병 예측방법의 연구가 활발히 진행되고 있다. 본 논문에서는 수산 양식장에서 발생할 수 있는 넙치의 질병을 딥러닝 기술로 예측하는 방법에 대한 연구결과를 소개하고자 한다. 이 방법은 양식장에서 수집된 카메라 영상에 데이터 증강과 전처리 포함하여 질병 인식률의 성능을 높인다. 이것을 통해 질병 어류를 조기 발견으로 양식 어업에서 어류 집단 폐사 등 어업 재해를 예방하고 지역 수산 양식장으로 어류의 질병 확산 피해를 줄여 매출액 감소 차단될 것으로 기대한다.

• 한국식물병리학회지
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• 제14권2호
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• pp.150-156
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• 1998

To develop a model for prediction of turnip mosaic virus(TuMV) disease progress of Chinese cabbage based on weather information and number of TuMV vector aphids trapped in Taegwallyeong alpine area, data were statistically processed together. As the variables influenced on TuMV disease progress, cumulative portion(CPT) above 13$^{\circ}C$ in daily average temperature was the most significant, and solar radiation, duration of sunshine, vector aphids and cumulative temperature above $0^{\circ}C$ were significant. When logistic model and Gompertz model were compared by detemining goodness of fit for TuMV disease progress using CPT as independent variable, regression coefficient was higher in the logistic model than in the Gompertz model. Epidemic parameters, apparent infection rate and initial value of logistic model, were estimated by examining the relationship between disease proportion linearized by logit transformation equation, In(Y/Yf-Y) and CPT. Models able to describe the progression of TuMV disease were formulated in Y=100/(1+128.4 exp(-0.013.CPT.(-1(1/(1+66.7.exp(-0.11.day). Calculated disease progress from the model was in good agreement with investigated actual disease progress showing high significance of the coefficient of determination with 0.710.

• 한국빅데이터학회지
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• 제2권2호
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• pp.115-128
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• 2017

최근 머신러닝 방법은 높은 예측력과 함께 널리 이용되지만 머신러닝을 제대로 활용하기 위해서 데이터가 가진 한계를 통계적 기법으로 해결한다면 기존보다 더 높은 예측력을 이끌어 낼 수 있다. 본 연구에서는 Longitudinal and Imbalanced Data에서 SMOTE 방법을 활용하여 불균형 문제를 해결한 결과 예측력이 증가하는 것을 확인할 수 있었다. 추가적으로 만성폐쇄성폐질환 급성악화 관련 연구가 활발히 이루어지고 있지만 급성악화와 관련 있는 요인을 찾는 연구만 이루어지고 있어 여러 요인들에 대한 복합적인 관철과 예측모형을 통한 급성악화 예측 연구는 이루어지지 않는다. 본 연구에서는 여러 요인을 같이 살펴봤을 때 어떤 요인들이 만성폐쇄성폐질환 급성악화와 관련이 있는지 확인하고 개인 맞춤형 특정 질환 예측 모형을 구축하였다.

• Safety and Health at Work
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• 제3권2호
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• pp.92-100
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• 2012

Objectives: The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease ${\times}$ exposure associations in RNV3P database (by analogy with the detection of potentially new health event ${\times}$ drug associations in the spontaneous reporting databases from pharmacovigilance). Methods: 2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease ${\times}$ exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease ${\times}$ exposure associations, are compared. Results: RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested. Conclusion: This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.

• 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.

• International Journal of Industrial Entomology and Biomaterials
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• 제22권1호
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• pp.11-15
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• 2011

Impact of pruning date, shoot age and weather parameters on the severity and development of grey leaf spot (Pseudocercospora mori) of mulberry was studied. The disease severity (%) increased with increase in shoot age irrespective of pruning date. Maximum disease severity was observed in plants pruned during second week of October and minimum in plants pruned during last week of December. Significant (P<0.05) influence of date of pruning, shoot age and their interaction was observed on the severity of the disease. Apparent infection rate (r) was significantly higher during plant growth period from day-48 to day-55. Average apparent rate was higher in plants pruned during first week of September and least in plants pruned during third and fourth week of December. Multiple regression analysis revealed contribution of various combinations of weather parameters on the disease severity. A linear prediction model [$Y=66.05+(-1.39)x_1+(-0.219)x_4$] with significant $R^2$ was developed for prediction of the disease under natural epiphytotic condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• Genomics & Informatics
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• 제6권3호
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• pp.136-141
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• 2008

A large number of studies have been performed to identify biomarkers that will allow efficient detection and determination of the precise status of a patient’s disease. The use of microarrays to assess biomarker status is expected to improve prediction accuracies, because a whole-genome approach is used. Despite their potential, however, patient samples can differ with respect to biomarker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different experimental conditions. Because of this difficulty in experimental standardization of microarray data, it is currently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a method that predicts disease status using gene expression data that are transformed by their ranks, a concept that is easily applied to two datasets that are obtained using different experimental platforms. NCI and colon cancer datasets, which were assessed using both Affymetrix and cDNA microarray platforms, were used for method validation. Our results demonstrate that the proposed method is able to achieve good predictive performance for datasets that are obtained under different experimental conditions.

• 한국빅데이터학회지
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• 제6권2호
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• pp.161-168
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• 2021

본 연구에서는 기계 학습을 사용하여 심혈관 질환 예측 모델을 제안한다. 먼저 두 집단간에 다양한 차이를 다차원분석하고 그 결과를 시각화한다. 특히, 질환과 같이 정상집단과 환자집단 간에 높은 클래스 불균형이 존재하는 경우에 대하여 민감도를 향상시킬 수 있는 비용 민감 학습을 사용하는 예측 모델을 제안한다. 본 연구에서는 대표적인 머신러닝 기술인 CART와 XGBoost를 사용하여 예측모델을 개발하고, 심혈관 질환 환자 데이터를 대상으로 예측하고 성능을 비교한다. 연구결과에 따르면 CART가 XGBoost 보다 더 높은 정확도와 특이도를 보였으며, 정확도는 약 70%~74%로 나타났다.