• Journal of Digital Convergence
• /
• v.19 no.9
• /
• pp.257-269
• /
• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• Journal of Digital Convergence
• /
• v.21 no.3
• /
• pp.33-39
• /
• 2023

This study is a performance evaluation of a blood sugar monitoring system that combines a PPG sensor, which is an evaluation device for blood glucose monitoring, and a DNN algorithm when monitoring capillary blood glucose. The study is a researcher-led clinical trial conducted on participants from September 2023 to November 2023. PPG-BGMS compared predicted blood sugar levels for evaluation using 1-minute heart rate and heart rate variability information and the DNN prediction algorithm with capillary blood glucose levels measured with a blood glucose meter of the standard personal blood sugar management system. Of the 100 participants, 50 had type 2 diabetes (T2DM), and the average age was 67 years (range, 28 to 89 years). It was found that 100% of the predicted blood sugar level of PPG-BGMS was distributed in the A+B area of the Clarke error grid and Parker(Consensus) error grid. The MARD value of PPG-BGMS predicted blood glucose is 5.3 ± 4.0%. Consequentially, the non-blood-based PPG-BGMS was found to be non-inferior to the instantaneous blood sugar level of the clinical standard blood-based personal blood glucose measurement system.

• Journal of Digital Policy
• /
• v.1 no.2
• /
• pp.61-69
• /
• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.265-267
• /
• 2004

경제 여건의 향상 및 생활양식의 변화로 최근 우리나라에서도 당뇨병 환자가 늘어남에 따라 당뇨병의 예측 및 치료가 중요한 관심사가 되고 있다. 본 논문은 1993년과 1995년 두 차례에 걸쳐 경기도 연천 지역 주민들의 여러 가지 신체 지수 등을 조사한 데이터를 대상으로, 1차 년도의 데이터로부터 동일한 환자가 2차 년도에 정상상태를 유지하는지 흑은 당뇨병으로 진행이 되는지를 예측하는 문제를 다룬다. 혈당량, 허리둘레 등의 수치가 당뇨병의 발병에 영향을 끼치는 것은 알려진 사실이므로, 현재의 데이터로부터 앞으로의 발병 가능성을 예측하는 것이 가능하며, 이는 환자에게 보다 정확한 정보를 알려줄 수 있으므로 의미가 있는 일이다. 예측을 위해 본 논문에서는 분류기를 사용하며, 예측율을 높이기 위해 여러 분류기를 BKS로 결합하였다. BKS (behavior knowledge space) 결합 방법은 분류기간의 독립 가정이 필요 없으며, 데이터 크기가 크고 전형적인 경우에 좋은 결과를 낼 수 있는 방법이다. BKS 결합 방법을 통해 실험을 해본 결과 단일 분류기로 실험을 한 결과보다 향상된 성능을 얻을 수 있었으며, 투표 결합 방법과 비교하여 더 좋은 성능을 보였다.

• Journal of the Korean Society of Radiology
• /
• v.10 no.3
• /
• pp.187-193
• /
• 2016

In this study, we performed the carotid artery ultrasound targeting 140 subjects who have conducted to evaluate the changes in intima-media thickness(IMT) and plaque correlated with the presence or absence of a hematological test of the carotid artery. Considering that the IMT thickness more than 1mm is abnormal based on the carotid artery ultrasound to assess the presence or absence of plaque, and examined the correlation by classifying the blood lipid value and the fasting blood glucose level through the serum test. Consequently, the fasting blood glucose level is being analyzed as independent predictors of causing dental plaque(p=0.033), cut off value was determined as 126 mg/dL(sensitivity 56.25%, specificity 68.38%) in ROC curve analysis. Furthermore, the odds ratio appeared 1.01 times the value in the Logistic regression. Therefore, it seemed that the necessity to prospective studies in a number of subjects are considered, and also taking into account a number of blood test values along with the sonography of the carotid artery as a valuable part for effective primary prevention and follow-up observation of the cardiac and brain vascular disease is highly recommended.

• Journal of the Korean Society of Radiology
• /
• v.13 no.3
• /
• pp.339-348
• /
• 2019

The purpose of this study was to investigate the effect of carotid artery ultrasound Respectively. The carotid intima-media thickness is known to have a significant correlation with cardiovascular disease and cerebrovascular disease. We investigated the relationship between carotid intima - media thickness, body mass index, waist circumference, the blood lipid value, fasting blood glucose, glycated hemoglobin, and blood pressure using carotid artery ultrasound. The carotid artery ultrasound was considered to be abnormality of IMT thickness over 0.8 mm and the presence or absence of atherosclerotic plaque was evaluated. Serological tests were used to compare the geologic value, fasting blood glucose level, and glycated hemoglobin. As a result, waist circumference (=.022), low density cholesterol (=.004), fasting blood glucose level (.019), and glycemic index (.002) were analyzed as predictors of atherosclerosis. In the ROC curve analysis, sensitivity was 87.80% (95% CI: 73.8-95.9), specificity was 41.67% (95% CI: 30.2-53.9), sensitivity was 78.05% (95% CI: 62.4-89.4) in low density lipoprotein, Specificity was 50.00% (95% CI: 38.0-62.0), sensitivity was 73.11% (95% CI: 57.1-85.8), specificity was 61.11 (95% CI: 48.9-72.4) and sensitivity was 82.93%-91.8) and a specificity of 43.06% (31.4-55.3). In logistic regression analysis, the risk of atherosclerosis was 0.248 times at waist circumference (WC)> 76 cm, 3.475 times at low-density lipoprotein (LDL-C) ${\geq}124mg/dL$, 0.618 at HbA1c> 5.4% It appeared as a times. We suggest that prospective study of carotid artery ultrasound should be performed for the effective prevention of cardiovascular diseases.

• The Korean Journal of Applied Statistics
• /
• v.22 no.4
• /
• pp.771-780
• /
• 2009

Cataract is the main cause of blindness and visual impairment, especially, age-related cataract accounts for about half of the 32 million cases of blindness worldwide. As the life expectancy and the expansion of the elderly population are increasing, the cases of cataract increase as well, which causes a serious economic and social problem throughout the country. However, the incidence of cataract can be reduced dramatically through early diagnosis and prevention. In this study, we developed a prediction model of cataracts for early diagnosis using hospital data of 3,237 subjects who received the screening test first and then later visited medical center for cataract check-ups cataract between 1994 and 2005. To develop the prediction model, we used random forests and compared the predictive performance of this model with other common discriminant models such as logistic regression, discriminant model, decision tree, naive Bayes, and two popular ensemble model, bagging and arcing. The accuracy of random forests was 67.16%, sensitivity was 72.28%, and main factors included in this model were age, diabetes, WBC, platelet, triglyceride, BMI and so on. The results showed that it could predict about 70% of cataract existence by screening test without any information from direct eye examination by ophthalmologist. We expect that our model may contribute to diagnose cataract and help preventing cataract in early stages.

• The Journal of the Korea Contents Association
• /
• v.10 no.7
• /
• pp.268-275
• /
• 2010

Coronary artery calcium scores(CACS) has been used as surrogate marker for coronary atherosclerosis. We evaluated 1042 patients who visited the Department of Health Promotion Center in Chonnam National University Hospital and had a test of the CACS from January 2006, to December 2008. This study was performed to evaluate the relation of the CACS with Cadiovascular disease(CVD) risk factors and FRS. CACS and FRS was a significant difference between the group whose calcium score was 0 and the group whose calcium scores were 1 in case of men 2.38(95% CI, 1.83-3.11), women 2.12(95% CI, 1.03-4.35). The age-and sex-adjusted odds ratios for predictor of CVD risk factors to women with age was 1.10(95% CI, 1.06-1.15), HDL-cholesterol was 2.38(95% CI, 1.04-5.44), Fasting plasma glucose was 2.89(95% CI, 1.16-7.21), to men with age was 1.11(95% CI, 1.08-1.14), LDL-cholesterol was 2.12(95% CI, 1.28-3.50), gamma-GTP was 1.73(95% CI, 1.17-2.55), Diabetes mellitus medication was 3.92(95% CI, 1.73-8.89). The CACS seems to be a siginificant factor to evaluate the CVD risk factors.

• Neonatal Medicine
• /
• v.18 no.1
• /
• pp.49-58
• /
• 2011

Purpose: The purpose of this study was to evaluate the relationship between cord blood adiponectin and insulin-like growth factor (IGF)-I and their effect on fetal growth and insulin resistance in mothers with gestational diabetes mellitus (GDM). Methods: Cord blood adiponectin and IGF-I were compared between mothers with GDM (GDM group, N=53) and controls (non-GDM group, N=101). Neonates were classified into three groups of small for gestational age (SGA, N=26), appropriate for gestational age (AGA, N=97), and large for gestational age (LGA, N=31) by birth weight. The association between cord adiponectin and IGF-I levels was evaluated in relation to maternal and neonatal clinical data. Results: Cord adiponectin was lower in the GDM group than in the non-GDM group (P<0.001). There was no significant difference in cord adiponectin among the SGA, AGA, and LGA groups in the GDM group (P=0.228). The cord adiponectin of AGA in the GDM group was significantly lower than that in the non-GDM group (P<0.001). The most powerful predictor affecting cord adiponectin was the result of maternal 75 g oral glucose tolerance test. The cord IGF-I values between the GDM group and the non-GDM group were not different (P=0.834). Neonates with the heavier birth weight had the higher cord IGF-I levels. The most powerful predictor affecting cord IGF-I was birth weight and the next was maternal parity. Conclusion: Both cord blood adiponectin and IGF-I were associated with fetal growth, but IGF-I was a more general and direct factor affecting fetal body size, and adiponectin seemed to have more association with insulin sensitivity than growth.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.11
• /
• pp.433-440
• /
• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.