• Journal of KIISE:Computer Systems and Theory
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• v.34 no.7
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• pp.276-281
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• 2007

In this paper, we use Support Vector Machine to predict the susceptibility of chronic hepatitis from single nucleotide polymorphism data. Our data set consists of SNP data for 328 patients based on 28 SNPs and patients classes(chronic hepatitis, healthy). We use leave-one-out cross validation method for estimation of the accuracy. The experimental results show that SVM with SNP is capable of classifying the SNP data successfully for chronic hepatitis susceptibility with accuracy value of 67.1%. The accuracy of all SNPs with health related feature(sex, age) is improved more than 7%(accuracy 74.9%). This result shows that the accuracy of predicting susceptibility can be improved with health related features. With more SNPs and other health related features, SVM prediction of SNP data is a potential tool for chronic hepatitis susceptibility.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.342-348
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• 2011

The study aimed to compare the validity between the abdominal ultrasonographic(US) grading system of fatty liver and histologic grading system of fatty liver in living liver donor candidates. As the fatty liver is defined as pathologic total fat >10%, US validity was sensitivity 64.6%, specificity 68%, positive predictive value 76.8%, negative predictive value 54%. As the strict data handling on US grading normal, mild fatty liver are negative, moderate fatty liver is positive, US validity was sensitivity 26.8%, specificity 100%, positive predictive value 100%, negative predictive value 45.5%. ROC curve analysis according to different cut off value of liver-to-kidney brightness ratio was Area under ROC curve=0.859(95% CI=0.795~0.922, state variable= total fat 10%). There were statistically significant difference( p<0.001). Ultrasonography for the fatty diagnosis showed a high validity to predict the result of histology grade of fatty liver.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.289-294
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• 2020

Peptic ulcer disease is a gastrointestinal disorder caused by Helicobacter pylori infection and the use of nonsteroid anti-inflammatory drugs. While many studies have been conducted to find the risk factors of peptic ulcers, there are no studies on the suggestion of peptic ulcer prediction models for Koreans. Therefore, the purpose of this study is to implement peptic ulcer prediction model using machine learning based on demographic information, obesity information, blood information, and nutritional information for middle-aged and elderly people. For model building, wrapper-based variable selection method and naive Bayes algorithm were used. The classification accuracy of the female prediction model was the area under the receiver operating characteristics curve (AUC) of 0.712, and males showed an AUC of 0.674, which is lower than that of females. These results can be used for prediction and prevention of peptic ulcers in the middle and elderly people.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.357-358
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• 2022

이 논문은 Deep Learning 을 이용해 의사의 진단의 도움을 줄 수 있는 Clinic Decision Support System(CDSS) Framework 를 제안한다. 당뇨병, 고혈압, 고지혈증 같은 대사질환은 증상이 있는 경우도 있지만 없는 경우가 대부분이다.[1] 그렇기 때문에 원격으로 진료할 경우 대사질환에 대한 부분을 놓칠 수 있다. 이러한 부분을 챗봇이 의사에게 Deep Neural Network(DNN)으로 예측된 정보를 제공해 도움을 준다.

• 건강소식
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• v.11 no.8 s.105
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• pp.33-33
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• 1987

뇨검사는 검체의 채취가 쉽고 비교적 간단한 검사 방법으로, 많은 정보를 얻을 수 있으며, 국소적ㆍ전신적 질환의 조기발견 및 예측으로 만성화된 질병으로의 이환을 막을 수 있는 방법이다.

• The Journal of the Korean life insurance medical association
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• v.18
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• pp.11-14
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• 1999

• 건강소식
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• v.35 no.12
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• pp.16-17
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• 2011

골다공증은 골강도에 문제가 생겨서 골절 위험이 커지는 질환이다. 골강도는 골밀도와 골의 질로 결정되는데, 골강도의 70%는 골량 즉, 골밀도에 의존한다. 따라서 골밀도는 골다공성 골절을 가장 잘 예측할 수 있는 지표가 된다.

• Journal of the KSME
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• v.53 no.8
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• pp.34-37
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• 2013

이 글에서는 인체 근골격 동역학 모델을 이용하여, 인체 관절 내부의 하중을 예측하고 관절 질환의 원인을 분석하는 방법에 대한 최근 연구를 소개하고자 한다.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Journal of the Korean Society of Radiology
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• v.5 no.1
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• pp.19-25
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• 2011

Photoplethysmogram(PPG) is the method to obtain the biomedical signal using the linear relationships between the blood volume for changing the cardiac contraction and relaxation and the amount of light for absorbing the hemoglobin in the blood. In this paper, we proposed the analyzed results which show the heart rate variability and the distribution of heart rate for before and after using PPG. Moreover, this paper designed and implemented the system based on personal computer to predict cardiovascular disease in advance using the analyzed results for the autonomic balance from taking the spectral analysis of heart rate and the state of the blood vessel for analyzing APG(acceleration plethysmogram).