• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.150-156
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• 2023

For a doctor, diagnosing a patient's heart disease is not easy. It takes the ability and experience with high flying hours to be able to accurately diagnose the type of patient's heart disease based on the existing factors in the patient. Several studies have been carried out to develop tools to identify types of heart disease in patients. However, most only focus on the results of patient answers and lab results, the rest use only echocardiography data or electrocardiogram results. This research was conducted to test how accurate the results of the classification of heart disease by using two medical data, namely echocardiography and electrocardiogram. Three treatments were applied to the two medical data and analyzed using the decision tree approach. The first treatment was to build a classification model for types of heart disease based on echocardiography and electrocardiogram data, the second treatment only used echocardiography data and the third treatment only used electrocardiogram data. The results showed that the classification of types of heart disease in the first treatment had a higher level of accuracy than the second and third treatments. The accuracy level for the first, second and third treatment were 78.95%, 73.69% and 50%, respectively. This shows that in order to diagnose the type of patient's heart disease, it is advisable to look at the records of both the patient's medical data (echocardiography and electrocardiogram) to get an accurate level of diagnosis results that can be accounted for.

• 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.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• 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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• 제22권5호
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• pp.135-142
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• 2022

Despite global medical advancements, many patients are misdiagnosed, and more people are dying as a result. We must now develop techniques that provide the most accurate diagnosis of heart disease based on recorded data. To help immediate and accurate diagnose of heart disease, several data mining methods are accustomed to anticipating the disease. A large amount of clinical information offered data mining strategies to uncover the hidden pattern. This paper presents, comparison between different classification techniques, we applied on the same dataset to see what is the best. In the end, we found that the Random Forest algorithm had the best results.

• 한국정보통신학회논문지
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• 제20권11호
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• pp.2137-2142
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• 2016

심장에서 발생된 전류는 전위 뿐만 아니라 자기장을 생성한다. 본 논문에서는 61 채널 양자 간섭 장치 (SQUID) 시스템을 사용하여 심자도 (MCG)의 신호를 취득하고, 이것으로부터 임상적으로 유의하다고 생각되는 다양한 특징 파라미터를 계산한다. 이를 입력으로 신경회로망 알고리즘을 적용하여 허혈성 심장질환의 분류를 수행하였다. 심자도 신호는 전처리 과정을 통해 파라미터의 추출을 용이하게 하였다. 연구에 사용된 데이터는 정상인 10명과 안정형 협심 증세를 보이는 허혈성 심장질환 환자 10명분의 신호이다. 이들 신호로부터 임상적으로 유의한 특징점, 특징 간격 파라미터 및 진폭비를 추출하였다. 심자도 특징 파라미터를 신경회로망 입력으로 사용하여 허혈성 심장질환의 분류가 가능함을 보였다.

• 정보처리학회논문지D
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• 제11D권5호
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• pp.1031-1040
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• 2004

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 진단 결과의 부 정확성으로 인해 심전계에서는 외국의 진단 알고리즘을 사용하고 있다. 이 논문에서는 심전도 데이터의 수집에서부터 전 처리 과정 그리고 데이터마이닝을 이용한 심장 질환 패턴 분류 기법을 제안한다. 이 패턴 분류기법은 빈발 패턴 베이지안이며 기존의 나이브 베이지안과 빈발 패턴 마이닝의 통합이다. 빈발 패턴 베이지안은 훈련단계에서 탐사된 빈발 패턴들을 사용하여 Product Approximation 구성하므로써 클래스 조건 독립 가정을 가진 나이브 베이지안의 단점을 해결한다.

• Child Health Nursing Research
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• 제25권3호
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• pp.262-272
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• 2019

Purpose: We aimed to identify factors influencing physical activity in adolescents with complex congenital heart disease. Methods: We recruited 92 adolescents with complex congenital heart disease from a tertiary medical center in Seoul, measured their levels of physical activity, and identified factors that influenced their physical activity levels using the Global Physical Activity Questionnaire, the New York Heart Association classification, congenital heart disease complexity, the Self-Efficacy Scale, and the Parental Bonding Instrument scale. Stepwise multiple linear regression was used to determine factors influencing physical activity. Results: Total physical activity was higher in males than in females (t=4.46, p<.001). Adolescents who participated in school physical education classes engaged in more physical activity than those who did not (t=6.77, p<.001). Higher self-efficacy (${\beta}=.41$, p<.001), male gender (${\beta}=.37$, p<.001) and participation in school physical education classes (${\beta}=.19$, p=.042) were associated with a higher likelihood of engagement in physical activity. Conclusion: It is necessary to develop nursing interventions that enhance self-efficacy in order to promote physical activity in adolescents with complex congenital heart disease. Physical activity should also be promoted in an individualized manner, taking into account gender, disease severity, and parental attitude.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.