• Journal of Korean Biological Nursing Science
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• v.23 no.1
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• pp.31-42
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• 2021

Purpose: This study was performed to assess the level of blood glucose and to identify poor glycemic control groups among patients with type 2 diabetes mellitus (DM). Methods: Data of 1,022 Korean type 2 DM patients aged 30-64 years were extracted from the Korea National Health and Nutrition Examination Survey VII. Complex samples analysis and a decision-tree analysis were performed using the SPSS WIN 26.0 program. Results: The mean level of hemoglobin A1c (HbA1c) was 7.22±0.25%, and 69.0% of the participants showed abnormal glycemic control (HbA1c≥6.5%). The characteristics of participants associated with poor glycemic control groups were presented with six different pathways by the decision-tree analysis. Poor glycemic control groups were classified according to the patients' characteristics such as period after DM diagnosis, awareness of DM, sleep duration, gender, alcohol drinking, occupation, income status, low density lipoprotein-cholesterol, abdominal obesity, and number of walking days per week. Period of DM diagnosis with a cut-off point of 6 years was the most significant predictor of the poor glycemic control group. Conclusion: The findings showed the predictable characteristics of the poor glycemic control groups, and they can be used to screen the poor glycemic control groups among adults with type 2 DM.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1266-1271
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• 2022

Cats have strong wildness so they have a characteristic of hiding diseases well. The disease may have already worsened when the guardian finds out that the cat has a disease. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia, polyuria, and frequent urination more quickly. In this paper, 1) Efficient version of DeepLabCut for pose estimation, 2) YOLO v4 for object detection, 3) LSTM is used for behavior prediction, and 4) BoT-SORT is used for object tracking running on an artificial intelligence device. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the server system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.165-167
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• 2022

The proportion of cat cats among companion animals has been increasing at an average annual rate of 25.4% since 2012. Cats have strong wildness compared to dogs, so they have a characteristic of hiding diseases well. Therefore, when the guardian finds out that the cat has a disease, the disease may have already worsened. Symptoms such as anorexia (eating avoidance), vomiting, diarrhea, polydipsia, and polyuria in cats are some of the symptoms that appear in cat diseases such as diabetes, hyperthyroidism, renal failure, and panleukopenia. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia (drinking a lot of water), polyuria (a large amount of urine), and frequent urination (urinating frequently) more quickly. In this paper, 1) Efficient version of DeepLabCut for posture prediction running on an artificial intelligence server, 2) yolov4 for object detection, and 3) LSTM are used for behavior prediction. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the main server system.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.619-630
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• 2019

Dyslipidemia is a representative chronic disease affecting Koreans that requires continuous management. It is also a known risk factor for cardiovascular disease such as hypertension and diabetes. However, it is difficult to diagnose vascular disease without a medical examination. This study identifies risk factors for the recognition and prevention of dyslipidemia. By integrating them, we construct a statistical instrumental nomogram that can predict the incidence rate while visualizing. Data were from the Korean National Health and Nutrition Examination Survey (KNHANES) for 2013-2016. First, a chi-squared test identified twelve risk factors of dyslipidemia. We used a naïve Bayesian classifier model to construct a nomogram for the dyslipidemia. The constructed nomogram was verified using a receiver operating characteristics curve and calibration plot. Finally, we compared the logistic nomogram previously presented with the Bayesian nomogram proposed in this study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.125-132
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• 2022

The Ministry of Environment recently analyzed the output data of 10 fine dust measuring stations and, as a result, announced that about 60% had an error that the existing atmospheric measurement concentration was higher. In order to accurately predict fine dust, the wind direction and measurement position must be corrected. In this paper, in order to solve these problems, fuzzy rules are used to solve these problems. In addition, in order to calculate the fine particulate sensation index actually felt by pedestrians on the street, a computer simulation experiment was conducted to calculate the fine particulate sensation index in consideration of weather conditions, temperature conditions, humidity conditions, and wind conditions.

• Journal of radiological science and technology
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• v.33 no.1
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• pp.37-44
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• 2010

We sought to confirm an independent factor about in-stent restenosis (ISR) in the patients who underwent drug-eluting stent (DES) and know a possibility as a predictor of measured coronary artery calcium score by MDCT. A total of 178 patients (159 men, $61.7{\pm}10.0$ years of age) with 190 coronary artery lesions were included in this study out of 1,131 patients who underwent percutaneous coronary intervention (PCI) with DES implantation for significant stenosis on MDCT at Chonnam National University Hospital between May 2006 and May 2009. All lesions were divided into two groups with the presence of ISR : group I (re ISR, N = 57) and group II (no ISR, N = 133). Compared to group II, group I was more likely to be older ($65.8{\pm}9.0$ vs. $60.2{\pm}9.9$ years, p = 0.0001), diabetic (21.8% vs. 52.6%, p = 0.0001), have old myocardial infarction (8.8% vs. 2.3%, p = 0.040), left main stem disease (5.3% vs. 0.8%, p = 0.047), and smaller stent size ($3.1{\pm}0.3\;mm$ vs. $3.3{\pm}0.4\;mm$, p = 0.004). Group II was more likely to be smokers (19.3% vs. 42.1%, p = 0.003), have dyslipidemia (8.8% vs. 23.3%, p = 0.019). Left ventricular ejection fraction, lesion complexity, and stent length were not different between the two groups. Total CAC score was $389.3{\pm}458.3$ in group I and $371.2{\pm}500.8$ in group II (p = 0.185). No statistical difference was observed between the groups in CAC score in the culprit vessel, left main stem, left anterior descending artery, left circumflex artery, and right coronary artery. On multivariate logistic regression analysis, left main stem disease (OR = 168.0, 95% CI = 7.83-3,604.3, p = 0.001), male sex (OR = 36.5, 95% CI = 5.89-2,226.9, p = 0.0001), and the presence of diabetes (OR = 2.62, 95% CI = 1.071-6.450, p = 0.035) were independent predictors of ISR after DES implantation. In patients who underwent DES implantation for significant coronary stenosis on MDCT, ISR was associated with left main stem disease, male sex, and the presence of diabetes. However, CAC score by MDCT was not a predictor of ISR in this study population.

• Archives of Plastic Surgery
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• v.35 no.5
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• pp.491-494
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• 2008

Purpose: When deciding a treatment plan in diabetic foot ulcer patients, predicting a possibility of healing wounds is important since not a few patients have poor general condition to get successful wound healing. This study was planned to find out if a serum collagen level can be used as a predictor for healing wounds in diabetic foot patients. Methods: Fifty-seven patients, who visited our clinic from January to June, 2007 for treatment of diabetic foot ulcers, were included in this study. Serum levels of type I collagen were checked using carboxy terminal type I propeptide kits. Simultaneously serum levels of vitamin C and iron, cofactors of collagen synthesis, were checked. The patients were divided into two groups; a group of successfully healed wounds and the other of unhealed wounds. Serum levels of the parameters were compared between the 2 groups. Results: The serum level of collagen was $197.65{\pm}86.26ng/ml$ in a healed group and $87.91{\pm}28.76ng/ml$ in the unhealed group(p<0.05). The serum iron and vitamin C levels were did not show significant differences. Conclusion: The serum collagen level may predict healing or nonhealing wounds in diabetic foot ulcers.

• Journal of Platform Technology
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• v.10 no.1
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• pp.20-28
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• 2022

Artificial intelligence-based health data verification has become an essential element not only to help clinical research, but also to develop new treatments. Since the US Food and Drug Administration (FDA) approved the marketing of medical devices that detect mild abnormal diabetic retinopathy in adult diabetic patients using artificial intelligence in the field of medical diagnosis, tests using artificial intelligence have been increasing. In this study, an artificial intelligence model based on image classification was created using a Teachable Machine supported by Google, and a predictive model was completed through learning. This not only facilitates the early detection of cataracts among eye diseases occurring among patients with chronic diseases, but also serves as basic research for developing a digital personal health healthcare app for eye disease prevention as a healthcare program for eye health.

• Journal of Dairy Science and Biotechnology
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• v.32 no.1
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• pp.17-29
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• 2014

GRAS probiotics can be used to modulate intestinal microbiota and to alleviate various gastrointestinal disorders. In several recent studies, researchers have explored the potential expansion and usability of probiotics to reduce the risk factors associated with diseases, including obesity, hypercholesterolemia, arterial hypertension, hyperhomocysteinemia, and oxidative stress. In this review, our aim was to clarify the mechanism underlying interactions between hosts (animal or human) and probiotics and the beneficial effects of probiotics on human health.