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

• 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.

• Annals of Clinical Neurophysiology
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• v.16 no.2
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• pp.55-61
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• 2014

Background: Viruses can cause either meningitis or encephalitis. It is unclear why some people suffer from aseptic meningitis, and others acquire aseptic encephalitis when infected with the same viral pathogens. The aim of this study was to compare demographic and laboratory factors between patients with aseptic meningitis and encephalitis. Methods: The demographic and laboratory differences were analyzed according to age, sex, diabetes, hypertension, C-reactive protein in the blood, white blood cell and protein in the cerebrospinal fluid, and glucose ratio (cerebrospinal fluid/blood). Additionally, we analyzed the nation-wide differencesin age between the patients with aseptic meningitis and those with encephalitis in Korea. Results: The patients with aseptic encephalitis were older, more likely to have hypertension, and had higher levels of C-reactive protein than did the patients with aseptic meningitis. However, the numbers of white blood cells in the cerebrospinal fluid were significantly higher in the patients with meningitis than in the patients with encephalitis. Multivariable analysis revealed that age >49 years, hypertension and a C-reactive protein level >5.81 mg/dL were independent and significant variables in the prediction of aseptic encephalitis. Additionally, the patients with aseptic encephalitis were older than those with aseptic meningitis in the nation-wide Korean database. Conclusions: Older age, hypertension, and higher levels of C-reactive protein are useful factors for the prediction of aseptic encephalitis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.602-604
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• 2017

As human life span has increased, people have wanted to live healthier desires. Especially Korea has rapidly entered an aging society, leading to the burden of medical expenses to the increase of disease accompanying aging. To alleviate the burden of medical expenses, prediction and prevention are important rather than treatment of diseases. It is possible to predict and prevent diseases by measuring individual genetic information. In order to utilize individual's genetic information Korea's genetic information is grasped through SNP (800 thousand) and GWAS optimized for the discovery of genetic factors of phenotype and disease of Koreans, The genetic information of each individual is analyzed in the genetic (constitutional) characteristics of the individual. In this thesis we develop a classification index so that we can classify populations of specific chronic diseases (obesity, diabetes or cardiovascular system). Try to develop health care services to manage custom diet and exercise associated with chronic illness.

• Smart Media Journal
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• v.7 no.3
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• pp.22-28
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• 2018

Air pollution problems in Korea are gradually becoming a higher concern due to various reasons such as fine dust, causing anxiety among people with regard to their health. Although various studies have been carried out on the relationship between the influence of fine dust and a certain disease, they are mostly focusing on the analyzation that fine dust is related to specific illnesses such as respiratory and cardiovascular diseases, hypertension and diabetes. In this paper, we utilize the public data of medical history information to extract ten medical care subjects with the highest number of monthly care in 2016, and analyze the relation of fine dust with certain medical subjects using Pearson correlation coefficient. We also subdivide and analyze the correlation between fine dust and the medical subjects according to their gender and age. Middle-aged Female group with the strongest positive correlation between fine dust and the medical subjects is analyzed with the correlation from 2011 to 2015, with its relevance coefficient extracted by regression analysis in order to predict the correlation with the medical subjects according to the fine dust concentration.

• Korean Journal of Biological Psychiatry
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• v.10 no.1
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• pp.3-19
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• 2003

The use of atypical antipsychotics is limited by occurrence of adverse reactions such as weight gain, despite of their benefits. This article provides a comprehensive review and discussion of the most significant findings regarding obesity-related pathways and integrates these with the known mechanism of atypical antipsychotic action. The focus of this article is primarily on the genetics of obesity related pathways that may be disrupted by atypical antipsychotics. This review also discussed weight gain, hyperglycemia or occurrence of diabetes while being treated with atypical antipsychotics from the point of view of pharmacogenetics. Pharmacogenetic research seeks to uncover genetic factors that will help clinicians identify the best treatment strategies for their patients. It will aid clinically in the prediction of response and side effects, such as antipsychotic-induced weight gain, and minimize the current "trial and error" approach to prescribing in the near future. This article also presents the genetics of both central and peripheral pathways putatively involved in antipsychotic-induced weight gain while providing a comprehensive review of the obesity literature. This article also review obesity related candidate molecules which may be disrupted during atypical antipsychotic drug treatment.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.297-306
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• 2018

Cardiovascular disease (CVD) is the leading cause of death worldwide and has a high mortality rate after onset; therefore, the CVD management requires the development of treatment plans and the prediction of prevalence rates. In our study, age, income, education level, marriage status, diabetes, and obesity were identified as risk factors for CVD. Using these 6 factors, we proposed a nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model for CVD. The attributes for each factor were assigned point values between -100 and 100 by Bayes' theorem, and the negative or positive attributes for CVD were represented to the values. Additionally, the prevalence rate can be calculated even in cases with some missing attribute values. A receiver operation characteristic (ROC) curve and calibration plot verified the nomogram. Consequently, when the attribute values for these risk factors are known, the prevalence rate for CVD can be predicted using the proposed nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model.

• Journal of Biomedical Engineering Research
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• v.22 no.6
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• pp.497-502
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• 2001

Influence of other blond components on measuring glucose concentration was analyzed B)food phantom containing five major components was made. The prediction model was developed based on the measurement of absorption spectra including the first overtone glucose band, i.e.. 1500 ∼ 1850 nm. The concentrations were Predicted using the Partial least squares regression. Factor analysis based on Design of Experiment was Performed to study the influence of other components in predicting glucose concentration. Triglyceride does not influence. Albumin and globulin haute minor effects. However, hemoglobin showed substantial response and the compensation of hemoglobin concentration appears to be required for the model of glucose measurement.

• Genomics & Informatics
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• v.8 no.3
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• pp.142-149
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• 2010

In recent years, genome-wide association (GWA) studies have successfully led to many discoveries of genetic variants affecting common complex traits, including height, blood pressure, and diabetes. Although GWA studies have made much progress in finding single nucleotide polymorphisms (SNPs) associated with many complex traits, such SNPs have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. This is partly due to that fact that most current GWA studies have relied on single-marker approaches that identify single genetic factors individually and have limitations in considering the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and provide a better prediction of complex traits, since it utilizes combined information across variants. Recently, a new statistical method for joint identification of genetic variants for common complex traits via the elastic-net regularization method was proposed. In this study, we applied this joint identification approach to a large-scale GWA dataset (i.e., 8842 samples and 327,872 SNPs) in order to identify genetic variants of obesity for the Korean population. In addition, in order to test for the biological significance of the jointly identified SNPs, gene ontology and pathway enrichment analyses were further conducted.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.156-162
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• 2021

Objective: The aim of the present study was to evaluate the predictive capability of fasting-state measurements of glucose and insulin levels alone for abnormal glucose tolerance in women with polycystic ovary syndrome (PCOS). Methods: In total, 153 Korean women with PCOS were included in this study. The correlations between the 2-hour postload glucose (2-hr PG) level during the 75-g oral glucose tolerance test (OGTT) and other parameters were evaluated using Pearson correlation coefficients and linear regression analysis. The predictive accuracy of fasting glucose and insulin levels and other fasting-state indices for assessing insulin sensitivity derived from glucose and insulin levels for abnormal glucose tolerance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Significant correlations were observed between the 2-hr PG level and most fasting-state parameters in women with PCOS. However, the area under the ROC curve values for each fasting-state parameter for predicting abnormal glucose tolerance were all between 0.5 and 0.7 in the study participants, which falls into the "less accurate" category for prediction. Conclusion: Fasting-state measurements of glucose and insulin alone are not enough to predict abnormal glucose tolerance in women with PCOS. A standard OGTT is needed to screen for impaired glucose tolerance and type 2 diabetes mellitus in women with PCOS.