• 감성과학
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• 제10권2호
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• pp.147-154
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• 2007

심장의 활동을 기록한 심전도는 심장의 상태에 대한 가치 있는 임상 정보를 제공한다. 지금까지 심전도를 이용한 심장 질환 진단 알고리즘에 대한 많은 연구가 진행되어 왔으나, 심장 질환에 대한 국내 진단 결과의 부정확성 때문에 외국의 진단 알고리즘을 사용하고 있다. 이 논문에서는 원시 심전도 데이터로부터 심장 질환 진단의 파라미터인 ST-segment 추출 방법을 제안한다. ST-segment는 관상동맥 질환 예측에 활용되므로 데이터마이닝의 분류기법을 적용하여 질환을 예측한다. 또한 연관규칙 마이닝을 통해 환자들의 임상 데이터로부터 심장 질환자들의 임상적 특징을 예측한다.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.223-229
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• 2022

Early detection can help prevent heart disease, which is one of the most common reasons for death. This paper provides a clinical support model for predicting cardiac disease. The model is built using two publicly available data sets. The admissibility and application of the the model are justified by a sequence of tests. Implementation of the model and testing are also discussed

• ETRI Journal
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• 제37권2호
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• pp.222-232
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• 2015

In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.

• 대한핵의학회지
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• 제38권5호
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• pp.331-337
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• 2004

Cardiac neurotransmission imaging allows in vivo assessment of presynaptic reuptake, neurotransmitter storage and postsynaptic receptors. Among the various neurotransmitter, I-123 MIBG is most available and relatively well-established. Metaiodobenzylguanidine (MIBG) is an analogue of the false neurotransmitter guanethidine. It is taken up to adrenergic neurons by uptake-1 mechanism as same as norepinephrine. As tagged with I-123, it can be used to image sympathetic function in various organs including heart with planar or SPECT techniques. I-123 MIBG imaging has a unique advantage to evaluate myocardial neuronal activity in which the heart has no significant structural abnormality or even no functional derangement measured with other conventional examination. In patients with cardiomyopathy and heart failure, this imaging has most sensitive technique to predict prognosis and treatment response of betablocker or ACE inhibitor. In diabetic patients, it allow very early detection of autonomic neuropathy. In patients with dangerous arrhythmia such as ventricular tachycardia or fibrillation, MIBG imaging may be only an abnormal result among various exams. In patients with ischemic heart disease, sympathetic derangement may be used as the method of risk stratification. In heart transplanted patients, sympathetic reinnervation is well evaluated. Adriamycin-induced cardiotoxicity is detected earlier than ventricular dysfunction with sympathetic dysfunction. Neurodegenerative disorder such as Parkinson's disease or dementia with Lewy bodies has also cardiac sympathetic dysfunction. Noninvasive assessment of cardiac sympathetic nerve activity with I-123 MIBG imaging nay be improve understanding of the pathophysiology of cardiac disease and make a contribution to predict survival and therapy efficacy.

• Journal of Information Processing Systems
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• 제20권4호
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• pp.514-523
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• 2024

In the Internet of Medical Things, due to the sensitivity of medical information, data typically need to be retained locally. The training model of heart disease data can predict patients' physical health status effectively, thereby providing reliable disease information. It is crucial to make full use of multiple data sources in the Internet of Medical Things applications to improve model accuracy. As network communication speeds and computational capabilities continue to evolve, parties are storing data locally, and using privacy protection technology to exchange data in the communication process to construct models is receiving increasing attention. This shift toward secure and efficient data collaboration is expected to revolutionize computer modeling in the healthcare field by ensuring accuracy and privacy in the analysis of critical medical information. In this paper, we train and test a multiparty decision tree model for the Internet of Medical Things on a heart disease dataset to address the challenges associated with developing a practical and usable model while ensuring the protection of heart disease data. Experimental results demonstrate that the accuracy of our privacy protection method is as high as 93.24%, representing a difference of only 0.3% compared with a conventional plaintext algorithm.

• Journal of Preventive Medicine and Public Health
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• 제45권5호
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• pp.323-328
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• 2012

Objectives: Economic growth and development of medical technology help to improve the average life expectancy, but the western diet and rapid conversions to poor lifestyles lead an increasing risk of major chronic diseases. Coronary heart disease mortality in Korea has been on the increase, while showing a steady decline in the other industrialized countries. An age-period-cohort analysis can help understand the trends in mortality and predict the near future. Methods: We analyzed the time trends of ischemic heart disease mortality, which is on the increase, from 1985 to 2009 using an age-period-cohort model to characterize the effects of ischemic heart disease on changes in the mortality rate over time. Results: All three effects on total ischemic heart disease mortality were statistically significant. Regarding the period effect, the mortality rate was decreased slightly in 2000 to 2004, after it had continuously increased since the late 1980s that trend was similar in both sexes. The expected age effect was noticeable, starting from the mid-60's. In addition, the age effect in women was more remarkable than that in men. Women born from the early 1900s to 1925 observed an increase in ischemic heart mortality. That cohort effect showed significance only in women. Conclusions: The future cohort effect might have a lasting impact on the risk of ischemic heart disease in women with the increasing elderly population, and a national prevention policy is need to establish management of high risk by considering the age-period-cohort effect.

• International Journal of Advanced Culture Technology
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• 제5권3호
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• pp.40-45
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• 2017

This data mining technique was used to extract useful information from percutaneous coronary intervention data obtained from the US public data homepage. The experiment was performed by extracting data on the area, frequency of operation, and the number of deaths. It led us to finding of meaningful correlations, patterns, and trends using various algorithms, pattern techniques, and statistical techniques. In this paper, information is obtained through efficient decision tree and cluster analysis in predicting the incidence of percutaneous coronary intervention and mortality. In the cluster analysis, EM algorithm was used to evaluate the suitability of the algorithm for each situation based on performance tests and verification of results. In the cluster analysis, the experimental data were classified using the EM algorithm, and we evaluated which models are more effective in comparing functions. Using data mining technique, it was identified which areas had effective treatment techniques and which areas were vulnerable, and we can predict the frequency and mortality of percutaneous coronary intervention for heart disease.

• Journal of Chest Surgery
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• 제30권12호
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• pp.1205-1213
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• 1997

가톨릭대학교 의과대학 부속 성바오로 병원에서 1996년 1월부터 7월까지 개심술시 경식도심초음파를 이용하여 수술 중 관찰한 61명의 증례를 분석하여 다음과 같은 결과를 얻었다. 61명의 환자 중 질병 분류는 심장 판막 질환 25명, 관상동맥 질환 22명, 선천성 심질환 13명, 심장 판막 질환과 관상동맥 질환이 병발한 경우가 1명 이었다. 1. 경식도심초음파를 이용하여 선천성 심질환 1예(심실중격결손증 재수술)와 대동맥 판막 치환술을 시행한 환자 1예에서 일차 심정지에 의한 수술 후 잔존 단락과 판막 주위 누출이 확인되어 재심정지를 유도하여 수술을 완료하였다 2. 공기색전증의 발생을 전 예에서 방지할 수 있었다. 3. 경식도심초음파로 측정한 심박출량과 온도희석법으로 측정한 심박출량은 통계학적으로 의미있는 상관관계를 보여 경식도 초음파를 이용한 심박출량 감시가 의미 있음을 시사하였다(Linear regression analysis, p<0.001). 4. 관상동맥우회로술을 시행한 23명의 환자에서 수술 중 경식도 초음파도상 이상 벽운동을 보이는 분절의 도부타민 투여 전후와 관상동맥우회로술 후의 반응을 비교 관찰하였다. 저 용량 도부타민 검사는 통계적으 \ulcorner유의하였다(민감도 76%, 특이도 94.7%, 양성 예측율 95%, 음성 예측율 75%).

• 한국컴퓨터정보학회논문지
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• 제27권5호
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• pp.21-28
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• 2022

심혈관질환은 심장질환과 혈관질환 등 순환기계통에 생기는 모든 질병을 통칭한다. 심혈관질환은 2019년 사망의 1/3을 차지하는 전 세계 사망의 주요 원인이며, 사망자는 계속 증가하고 있다. 이와 같은 질병을 인공지능을 활용해 환자의 데이터로 미리 예측이 가능하다면 질병을 조기에 발견해 치료할 수 있을 것이다. 본 연구에서는 심혈관질환 중 하나인 심장질환을 예측하는 모델들을 생성하였으며 Accuracy, Precision, Recall의 측정값을 지표로 하여 모델들의 성능을 비교한다. 또한 Decision Tree의 성능을 향상시키는 방법에 대해 기술한다. 본 연구에서는 macOS Big Sur환경에서 Jupyter Notebook으로 Python을 사용해 scikit-learn, Keras, TensorFlow 라이브러리를 이용하여 실험을 진행하였다. 연구에 사용된 모델은 Decision Tree, KNN(K-Nearest Neighbor), SVM(Support Vector Machine), DNN(Deep Neural Network)으로 총 4가지 모델을 생성하였다. 모델들의 성능 비교 결과 Decision Tree 성능이 가장 높은 것으로 나타났다. 본 연구에서는 노드의 특성배치를 변경하고 트리의 최대 깊이를 3으로 지정한 Decision Tree를 사용하였을 때 가장 성능이 높은 것으로 나타났으므로 노드의 특성 배치 변경과 트리의 최대 깊이를 설정한 Decision Tree를 사용하는 것을 권장한다.

• 대한방사선기술학회지:방사선기술과학
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• 제47권2호
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• pp.125-135
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• 2024

This study analysed the factors that predict and influence heart disease through key indicators related to changes in left atrial and left ventricular size. Measurements recommended by the American Society of Echocardiography were used, and the influence of variables was assessed using multiple regression analysis. The results showed that left atrial volume index(LAVI) was significantly different by age, obesity, diabetes, hypertension, dyslipidaemia, and left ventricular relaxation dysfunction(p<0.05). Left ventricular mass index(LVMI) was significantly different according to age, body mass index, hypertension, diabetes, dyslipidaemia, and left ventricular relaxation dysfunction(p<0.05). Increases in LVMI and relative ventricular wall thickness(RWT) were associated with changes in LAVI(p<0.05). Age, systolic blood pressure, increased LAVI, and RWT influenced changes in LVMI, and left ventricular dysfunction was analysed as an influencing factor for both changes in LAVI and LVMI. Therefore, changes in left atrial and left ventricular size are indicators for early diagnosis and prevention of heart disease, and it is necessary to carefully observe structural changes in the heart and actively manage risk factors for the prevention and management of heart disease.