Detection Of Heart Disease Research Articles

Education and early detection of coronary heart disease in the community

Coronary heart disease (CHD), commonly called coronary artery disease, refers to pathological processes that affect the coronary arteries, usually caused by atherosclerosis. One of the factors that influence the occurrence of atherosclerosis is the influence of people's lifestyles, the level of awareness of early detection of coronary heart disease, and the wrong dietary arrangements that cause a high risk of coronary heart disease in the community. This community service aims to increase public knowledge about managing and preventing coronary heart disease. The method used is education using media and early detection through health checks that can affect the occurrence of coronary heart disease, such as blood pressure, blood sugar values, and cholesterol. Knowledge of the risks of PKJ and early detection is expected to increase public awareness. It can be concluded that the activities of providing public education and early detection with health checks produce output that there is an increase in knowledge. Education related to coronary heart disease and health checks is expected to help conduct routine monitoring by educating families about early signs and prevention of coronary heart disease

Early detection, diagnosis and treatment principal of congenital heart disease

Early detection, diagnosis and treatment principal of congenital heart disease

Portable ECG monitoring system with IOT analysis

This system is designed to calculate the heart rate of patients and transmit the data in beats per minute (bpm) to a cloud database. By using this system, doctors can analyze critical parameters remotely. The project involves interfacing the AD8232 ECG Sensor with the NodeMCU ESP8266 Board to monitor ECG waveforms on a Serial Plotter Screen and transmit the data to an IoT cloud platform for real-time monitoring. This innovation allows for remote monitoring of heart activity, reducing the need for hospital stays. Utilizing the Blynk IoT platform, the system ensures online access via PC or Smartphone, representing a significant advancement in patient health monitoring. This approach aims to improve early detection and prevention of heart diseases, enhancing patient care and reducing fatalities.

A proposed model using Naïve Bayes and generalized linear models for early detection of heart attack risk

<span>Timely identification of diseases, particularly heart attacks is crucial for individuals, particularly the elderly, to accurately anticipate the onset of the disease based on symtoms. The objective of this study is to develop a highly accurate model for early detection of heart disease using the Naïve Bayes (<span>NB) and generalized linear model (GLM) techniques. In addition, another concern is the model’s subfar accuracy levels, promting the implementation of measures to optimize it. The suggested approach fot optimization involves</span> <span>the utilization of a genetic algorithm (GA). The research findings indicate that the NB and GLM approaches achive a reasonably high level of accuracy. Specifically, the NB model achieves an</span> <span>accuracy of 82.53%, while the GLM achieves an accuracy of 84.50%. Following optimization, the accuracy levels notably improved, with the NB_M-GA model reaching 85.83% and the GLM_M-GA model achieving 86,48%.</span></span>

Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts

Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.

Early Detection and Control of Risk Factors for Cardiovascular Diseases in the Aral Region: Experience of Uzbekistan.

To analyze the results of screening of the population older than 40 years for early detection of risk factors for cardiovascular diseases in real clinical practice of family clinics in the Aral Sea region. The results of screening of the population older than 40 years were analyzed for a total of 2,430 respondents from family clinics of the district (Republic of Uzbekistan, Republic of Karakalpakstan, Ellikkala district) according to the modified WHO PEN protocol. 1,020 of the respondents with blood pressure ≥140/90 mm Hg were included in the study (mean age, 57.68±8.06 years; women, 61.4%; men, 38.6%). Additionally, the following parameters were determined: salt-taste threshold using the R. Henkin method, echocardiography, ultrasonography of the brachiocephalic arteries, blood lipid spectrum, microalbuminuria, serum creatinine and uric acid. Statistical data are presented as mean±SD. The prevalence of signs in the study group was assessed using the Pearson's chi-square test, and the Pearson correlation coefficient was used. Among the patients with elevated blood pressure included in the study, 24 (2.4%) were younger than 40 years, 847 (81%) were 40-65 years old, and 169 (16.6%) were older than 65 years. Low cardiovascular risk was twice more common among women compared to men: 11.3% vs. 5.6% (χ²=8.990; p=0.003); almost 75% fewer patients with ischemic heart disease, 7.4% vs. 28.9% (χ²=14.939; p=0.0001); however, the incidence of type 2 diabetes mellitus was twice higher, 13.7% vs. 7.4% (χ²=9.205; p=0.002); the female group had significantly fewer cases of postinfarction cardiosclerosis (PICS) (χ²=5.313; p=0.021). Among women, there were no tobacco users or regular alcohol drinkers whereas among men these risk factors were identified in 59.4% (χ²=178.848; p=0.0001) and 35% (χ²=82.238; p=0.0001), respectively. 85.6% of the respondents had a high salt-taste threshold, 96% had left ventricular hypertrophy, 76% had microalbuminuria, 21% had proteinuria, and 92% of both men and women had a common carotid artery intima-media thickening >0.9 mm. The study showed a broad prevalence of cardiovascular risk factors in the population of hypertensive patients in the Aral region, a high salt-taste threshold, and significant damages to target organs, which differed from other regions of Uzbekistan. Among hypertensive men, there was a significant prevalence of tobacco and alcohol use, and a significantly more frequent detection of ischemic heart disease, PICS and hyperuricemia compared to women; in the female population, the prevalence of type 2 diabetes mellitus was significantly greater.

Hybrid optimized temporal convolutional networks with long short-term memory for heart disease prediction with deep features

A heart attack is intended as top prevalent among all ruinous ailments. Day by day, the number of affected people count is increasing globally. The medical field is struggling to detect heart disease in the initial step. Early prediction can help patients to save their life. Thus, this paper implements a novel heart disease prediction model with the help of a hybrid deep learning strategy. The developed framework consists of various steps like (i) Data collection, (ii) Deep feature extraction, and (iii) Disease prediction. Initially, the standard medical data from various patients are acquired from the clinical standard datasets. Here, a One-Dimensional Convolutional Neural Network (1DCNN) is utilized for extracting the deep features from the acquired medical data to minimize the number of redundant data from the gathered large-scale data. The acquired deep features are directly fed to the Hybrid Optimized Deep Classifier (HODC) with the integration of Temporal Convolutional Networks (TCN) with Long Short-Term Memory (LSTM), where the parameters in both classifiers are optimized using the newly suggested Enhanced Forensic-Based Investigation (EFBI) inspired meta-optimization algorithm. Throughout the result analysis, the accuracy and precision rate of the offered approach is 98.67% and 99.48%. The evaluation outcomes show that the recommended system outperforms the extant systems in terms of performance metrics examination.

Clinical Value of Echocardiographic Screening and Dynamic Observations for Ductus Arteriosus Closure in Newborns

Objective: To explore and analyze the clinical value of echocardiography screening and dynamic observation for ductus arteriosus closure in newborns. Methods: The study was conducted from August 2022 to December 2023. 500 newborns who were admitted to our hospital were selected as research subjects for this study (Kunming Tongren Hospital). All subjects underwent echocardiography to determine the severity of patent ductus arteriosus (PDA). The inner diameter of the pulmonary artery was measured, and the pulmonary artery pressure was estimated through tricuspid regurgitation. If the patient had high pulmonary artery pressure, elective closure surgery was performed. Results: Among the 500 subjects, 448 cases of ductus arteriosus closure and 52 cases of PDA were detected by echocardiography, including 23 cases of simple PDA, 15 cases with patent foramen ovale, 11 cases with atrial septal defect, 1 case with muscular ventricular septal defect, 1 case with tricuspid valve prolapse, and 1 case with tricuspid valve chordae tendineae rupture. After symptomatic treatment and intervention, their echocardiogram results were reviewed and no abnormalities were found. All measurement results of children with PDA combined with pulmonary hypertension were better than before treatment (P < 0.05). Conclusion: Echocardiography is a fast, accurate, and reliable non-invasive imaging examination technology. It can significantly improve congenital heart disease detection rate in infants and young children. Early intervention based on the examination results can dramatically improve the quality of life of infants and young children with PDA.

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.

PENGELOMPOKAN TINGKAT RESIKO PENYAKIT JANTUNG BERDASARKAN USIA MENGGUNAKAN ALGORITMA K-MEANS

Heart disease is one of the non-communicable diseases that can cause death, This disease occurs due to a narrowing of the blood vessels so as to cause impaired heart function Some of the causes of heart disease are one of them based on age, basically heart disease can be prevented by various factors including a healthy lifestyle, besides that early detection of heart disease is also needed to prevent death in sufferers One way to do early detection is to use data mining. The use of the k-means algorithm can be done to cluster the grouping of heart diseases by age to find out someone is exposed to the cause of high and low heart disease. Based on these problems, this study uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method with several stages such as Business Understanding, Data Understanding, Data preparation, Modeling, Evaluation, and Deployment. The clustering method with the k-means algorithm in this study shows a new insight, namely grouping the risk level of heart disease based on 3 clusters. Cluster 0 is an age category with a fairly low risk level of heart disease or Low, which is 355 out of 1025 age categories tested, then cluster 1 is an age category with a moderate or Medium heart disease risk level, which is 208 out of 1025 age categories tested, and finally cluster 2 is an age category with a fairly high age category or High, which is 462 out of 1025 age categories tested.

An offbeat bolstered swarm integrated ensemble learning (BSEL) model for heart disease diagnosis and classification

In recent years, cardiovascular disease also known as heart disease has emerged as the top cause of mortality throughout the world. Several different cardiac conditions are included. There are many variables that might increase a person's likelihood of developing heart disease; thus, it is critical to develop reliable, rapid methods of diagnosis and treatment. Therefore, the proposed work aims to develop an effective framework, named as, Bolstered Swarm Integrated Ensemble Learning (BSEL) for heart disease detection. Here, the given medical dataset has been cleaned up, transformed, and normalized using the Linear Interpolation Normalization (LIN) technique. For selecting the best features, the Bolstered-up Beetle Swarm Optimization (BBSO) method is used that removes the irrelevant attributes from the standardized datasets. In addition, the best features are employed in the Weighted Ensemble Classification (WEC) model to establish whether or not a patient has heart disease. The accuracy is improved by using an ensemble classifier with the optimal number of hidden neurons, as determined by the Red Colobuses Monkey Optimization (RCoMO) model. The proposed BSEL prediction model's results are tested and assessed during experiment using a variety of performance measures.

Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation.

Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms. We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (P value=0.83) and a 9-feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87. Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.

Role of artificial intelligence in early detection of congenital heart diseases in neonates.

In the domain of healthcare, most importantly pediatric healthcare, the role of artificial intelligence (AI) has significantly impacted the medical field. Congenital heart diseases represent a group of heart diseases that are known to be some of the most critical cardiac conditions present at birth. These heart diseases need a swift diagnosis as well as an intervention to ensure the wellbeing of newborns. Fortunately, with the help of AI, including the highly advanced algorithms, analytics and imaging involved, it provides us with a promising era for neonatal care. This article reviewed published data in PubMed, Science Direct, UpToDate, and Google Scholar between the years 2015-2023. To conclude The use of artificial intelligence in detecting congenital heart diseases has shown great promise in improving the accuracy and efficiency of diagnosis. Several studies have demonstrated the efficacy of AI-based approaches for diagnosing congenital heart diseases, with results indicating that the systems can achieve high levels of sensitivity and specificity. In addition, AI can help reduce the workload of healthcare professionals allowing them to focus on other critical aspects of patient care. Despite the potential benefits of using AI, in addition to detecting congenital heart disease, there are still some challenges to overcome, such as the need for large amounts of high-quality data and the requirement for careful validation of the algorithms. Nevertheless, with ongoing research and development, AI is likely to become an increasingly valuable tool for improving the diagnosis and treatment of congenital heart diseases.

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study's primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study's outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model's diagnostic accuracy for heart disease.

A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing

Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPM’s effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women.

ECG Based Heart Disease Classification: Advancement and Review of Techniques

The heart plays a crucial role in pumping blood to our organs. However, without early detection of heart disease (HD), the outcomes can be fatal. That’s why it is crucial to emphasize the significance of prior detection and accurate classification of HD for effective diagnosis. One important diagnostic technique that has emerged is electrocardiography (ECG). Analyzing and processing ECG signals greatly contribute to diagnosing HD. In this comprehensive review, we discuss recent advancements in HD classification using various machine learning, deep learning, and ensemble learning algorithms such as SVM, CNN, XGBoost, etc. We delve into the advantages and disadvantages of each algorithm while providing an extensive overview of state-of-the-art studies that have utilized these algorithms on datasets MIT-BIH and PTB-XL. Among these models, CNN consistently achieved remarkable accuracy across both datasets with an average of 98.83%. The LSTM model excelled in sensitivity on the PTB-XL dataset with an average recall rate of 88.01%. Additionally, SVM and k-NN models delivered competitive accuracy rates of 96.20% and 97.50%, respectively, on the MIT-BIH dataset. We also explore GMM and K-Means clustering techniques to provide insights into their potential applications in classifying HD. It’s worth noting that existing research often focuses on individual methods without comprehensive comparisons between available algorithms. Our paper aims to bridge this gap by offering a concise overview that encompasses various methods’ strengths, limitations, and advancements, providing valuable insights for future studies.

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Heart disease detection system

This paper presents a comprehensive exploration into the utilization of machine learning (ML) techniques to revolutionize medical diagnostics, with a specific focus on enhancing the detection of heart disease. Recognizing the imperative need for early diagnosis to address the global prevalence of heart disease, this study delves into the development and application of advanced ML principles. The paper aims to construct a robust ML model capable of analyzing diverse patient data sets, including electronic health records and genetic information, to discern intricate patterns and correlations imperceptible to human clinicians. By leveraging a comprehensive dataset encompassing various patient profiles, the ML model is poised to significantly enhance the precision, speed, and efficiency of heart disease detection. The findings of this paper hold promise for fostering more effective intervention strategies and improving patient care outcomes in the realm of cardiovascular health.

Classification of Congestive Heart Failure Using Artificial Neural Network Based on Higher-Order Moments Detrended Fluctuation Analysis of Heart Rate Variability

Coronary heart disease or cardiovascular disease is a kind of heart disease that affects the heart and all the blood vessels in the body caused by a build-up of plaque in a person's arteries and can result in a stroke or heart attack. According to the Indonesian Ministry of Health, (Depkes RI) coronary heart disease is the main and first cause of all deaths by heart disease, which account for 26.4% among all other causes. Considering the severity of the condition, it is necessary to detect this kind of heart disease to reduce the number of deaths from heart disease. Heart disease detection can be performed by checking the Heart Rate Variability (HRV) signal. HRV assessment is carried out by analyzing short-term and long-term Electrocardiogram (ECG) records. To analyze HRV signals, a Higher-Order Moments Detrended Fluctuation Analysis feature extraction method is proposed to see the non-linear structure of HRV data. The input for Higher-Order Moments Detrended Fluctuation Analysis is an ECG signal which has been converted into an HRV signal. The output of the feature extraction shows that the value of kurtosis-based fluctuation function has a statistical significance in order to differentiate the HRV of heart disease patients and that of normal subjects. These values are then used as the input for classification using an Artificial Neural Network. The output of the classification is the classification of patients with heart disease and normal subjects. The results of the Higher-Order Moments Detrended Fluctuation Analysis feature extraction classification yield the best accuracy of 71.43% with a ROC value of 0.774 which can be categorized as a pretty good classification.

Veliki neželjeni kardiovaskularni događaji kod vaskularnih hirurških pacijenata sa perifernom arterijskom bolešću

Introduction: Major adverse cardiac events (MACE) are the primary cause of morbidity and mortality in patients undergoing vascular surgical procedures. This is why it is essential to understand the relationship between coronary artery disease and peripheral arterial disease. In this paper, we aimed to determine predictors and risk factors related to the occurrence of MACE in patients undergoing vascular surgery for peripheral arterial disease. Material and Methods: This is a retrospective study of 310 in-hospital patients treated at a high-volume vascular centre. The outcome was classified through the three-point MACE (myocardial infarction, stroke and cardiovascular mortality). Results: We found that the majority of initial MACE happened in patients with aortobifemoral bypass surgery compared to other types of reconstruction (p=0.009). No significant correlation was established between the procedures performed and the short-term primary outcome. In the initial period, MACE was detected in 14 (5.8%) patients. Among them, six (1.9%) experienced a myocardial infarction (MI), three (1%) had a stroke, and five (1.6%) died within the first 30 days. All deaths were linked to cardiac ischemia, which ultimately culminated in cardiogenic shock and subsequent death. Short term MACE was observed in 2 (0.6%) patients both of whom had an MI. Conclusion: Major vascular surgery in patients with coronary artery disease is a highly morbid procedure and perioperative MACE places them at a significantly high risk of mortality. Early detection of coronary heart disease and preoperative optimization can play a major role in reducing the risk of MACE.