Detection Of Heart Disease Research Articles

Optimized Approach for Heart Disease Prediction by using Enhancing Diagnostic Method through Meta-Features

In today's world, pollution is worsening, and bad habits like not eating regularly, eating lots of junk food, and not exercising enough are becoming more common. This can cause health problems, and the global need for the detection of heart diseases is increasing. The American Heart Association, in 2023, has given an update that heart disease is the leading cause of death. Implementing machine learning models has given significant results, but due to the limitations of requirement balanced data, repeat training model complexity has led to unreliable results in some cases. Hence, the proposed model overcomes the limitations by comparing machine learning models for cardiovascular diseases. Performance of proposed work is evaluated with the base models KNN and DT with and without smooth. Thus, the comparison included increased accuracy in DT, but the proposed model GBM as a meta learner has led the performance metric with 92% accuracy, with recall of 0.89% and F1 score of 0.86%. Thus, the proposed approach has achieved the highest accuracy in cardiovascular diseases using meta-subjects. Future developments for the research will focus on applying the model to a larger dataset and analysing cases according to complex machine learning models.

An Effective Heart Disease Detection and Classification using Machine Learning

About 20.5 million people die yearly due to cardiovascular disease (CVD). Early prediction of heart disease and accurate heart severity identification can save the induvial life with timely medications. In many cases, most deaths occur due to inaccurate diagnosis of the heart. In computer science, various existing researchers have dealt with the classification of heart disease on synthetic as well as real-time datasets. But still, those systems have a challenge such as low classification accuracy, high error rate and inaccurate heart severity detection. After identifying all these challenges, we proposed an effective heart disease detection and classification using hybrid machine learning techniques. In this article, we describe how various feature extraction and hybrid machine learning classifiers produce accurate severity of heart disease on real-time datasets. In the first phase, we collected a Cleveland dataset from Kaggle and then applied preprocessing and normalization for data balancing. Various feature extraction and selection methods, such as TF-IDF, Co-relation coefficient, N-Gram, and bi-Gram features, are used for practical module training. The different machine learning we used on normalized datasets for classification. The five supervised machine learning algorithms are used, such as Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and Hybrid Machine Learning (HML) etc. The HML achieves 99.30% accuracy on the Cleveland dataset using Weka 3.8 machine learning framework. As a result, the proposed system compares with various heart disease predictions using machine learning techniques.

Adapting transformer-based language models for heart disease detection and risk factors extraction

Efficiently treating cardiac patients before the onset of a heart attack relies on the precise prediction of heart disease. Identifying and detecting the risk factors for heart disease such as diabetes mellitus, Coronary Artery Disease (CAD), hyperlipidemia, hypertension, smoking, familial CAD history, obesity, and medications is critical for developing effective preventative and management measures. Although Electronic Health Records (EHRs) have emerged as valuable resources for identifying these risk factors, their unstructured format poses challenges for cardiologists in retrieving relevant information. This research proposed employing transfer learning techniques to automatically extract heart disease risk factors from EHRs. Leveraging transfer learning, a deep learning technique has demonstrated a significant performance in various clinical natural language processing (NLP) applications, particularly in heart disease risk prediction. This study explored the application of transformer-based language models, specifically utilizing pre-trained architectures like BERT (Bidirectional Encoder Representations from Transformers), RoBERTa, BioClinicalBERT, XLNet, and BioBERT for heart disease detection and extraction of related risk factors from clinical notes, using the i2b2 dataset. These transformer models are pre-trained on an extensive corpus of medical literature and clinical records to gain a deep understanding of contextualized language representations. Adapted models are then fine-tuned using annotated datasets specific to heart disease, such as the i2b2 dataset, enabling them to learn patterns and relationships within the domain. These models have demonstrated superior performance in extracting semantic information from EHRs, automating high-performance heart disease risk factor identification, and performing downstream NLP tasks within the clinical domain. This study proposed fine-tuned five widely used transformer-based models, namely BERT, RoBERTa, BioClinicalBERT, XLNet, and BioBERT, using the 2014 i2b2 clinical NLP challenge dataset. The fine-tuned models surpass conventional approaches in predicting the presence of heart disease risk factors with impressive accuracy. The RoBERTa model has achieved the highest performance, with micro F1-scores of 94.27%, while the BERT, BioClinicalBERT, XLNet, and BioBERT models have provided competitive performances with micro F1-scores of 93.73%, 94.03%, 93.97%, and 93.99%, respectively. Finally, a simple ensemble of the five transformer-based models has been proposed, which outperformed the most existing methods in heart disease risk fan, achieving a micro F1-Score of 94.26%. This study demonstrated the efficacy of transfer learning using transformer-based models in enhancing risk prediction and facilitating early intervention for heart disease prevention.

Heart Disease Detection Using Machine Learning

Abstract: One of the most common tasks inmachine learning is to classify data. Machine learning is a key feature to derive information from corporate operating datasets from large databases. Machine Learning in Medical Health Care is an essential emerging field for delivering prognosis anda deeper understanding of medical data. Most methods of machine learning depend on several features defining the behavior of the algorithm and influencing the output and the complexity of the resulting models directly or indirectly. In the last tenyears, heart disease is the world’s leading cause of death. Many machine learning methods have been used in the past to detect heart disease. Neural Network and Logistic Regression are some of the fewpopular machine learning methods used in heart disease diagnosis. They analyze multiple algorithms such as Neural Network, KNearest Neighbors, NaiveBayes, and Logistic Regression along withcomposite approaches incorporating the above-mentioned heart disease diagnostic algorithms. The system was implemented and trained in the Python platform using the UCI machine learning repository benchmark dataset. For the new data collection, the framework can be extended.

A Robust Predictive Model for Early Detection of Heart Disease using Machine Learning

Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning- based technique is needed to predict heart failure health status early and take necessary actions to overcome this worldwide issue. While medication is the primary treatment, exercise is increasingly recognized as an effective adjunct therapy in managing heart failure. This research project aims to develop a robust predictive model for the early detection of heart diseases by leveraging machine learning techniques, with a specific focus on the application of Support Vector Machines (SVM). The growth in technology has improved the information or data which can be extracted from a patient to help pinpoint the cause of illness. Using different number of attributes or data from the medical profile of a patient can predict the chance of a patient developing a heart condition. In simple terms, these attributes are loaded into logistic regression, Decision Tree, Random Forest, SVM, KNN and Naive bayes, that is, Machine learning (ML) algorithms for the analysis and further prediction of heart disease. There are many other techniques, methods used by other researchers. By using this method, the standards in the medical industries are elevated and rose as they can provide better diagnostics and treatment of the patient, resulting in providing an overall good quality service. This has its main focus towards: Using Data analysis, creating prediction Models to provide early detection of Heart Diseases, Also, by creating a reliable method to predict heart disease. Thus, we found that supervised machine learning algorithms can be used to make heart disease predictions with very high accuracy and excellent potential utility. Our proposed research study has significant scientific contributions to the medical community. Key Words: Machine learning, heart failure, cross validations, feature engineering, Naive Bayes, k Nearest Neighbor (KNN), Decision tree, Artificial Neural Network (ANN), Random Forest, Heart Disease.

Cardiovascular Disease Long-Term Care Risk Prediction by Claims Data Analysis Using Machine Learning

Heart disease is a major global health concern, especially in predicting cardiovascular issues. Machine learning (ML) and the Internet of Things (IoT) offer new ways to analyze healthcare data. However, current research lacks depth in using ML for heart disease prediction. To fill this gap, we propose a unique method that uses ML to identify key features for better heart disease prediction accuracy. Our model combines various features and classification techniques to achieve an accuracy of 88.7% in predicting heart disease, with the hybrid random forest and linear model (HRFLM) proving particularly effective. This study advances heart disease detection by integrating ML and IoT technologies.

Heart Disease Prediction using Machine Learning

Machine Learning (ML) is increasingly applied in various sectors globally, and the healthcare sector is no exception. In particular, ML can significantly contribute to the early detection of locomotor disorders and heart diseases. Timely predictions can offer valuable insights to physicians, enabling them to tailor their diagnostic and treatment strategies for individual patients. This project focuses on the use of ML algorithms to predict the likelihood of heart disease in individuals. It involves a comparative analysis of several classifiers, including decision trees, Naïve Bayes, Logistic Regression, SVM, and Random Forest. Furthermore, the project introduces an ensemble classifier that combines the strengths of both robust and less robust classifiers. This approach allows for the utilization of numerous samples for training and validation purposes. We analyze both existing classifiers and proposed classifiers like AdaBoost and XGBoost, aiming to enhance accuracy and predictive capabilities.Heart disease remains a significant concern worldwide, and early detection is crucial for preventing severe outcomes and enhancing patient care. Machine learning techniques have shown promise in increasing the accuracy of heart disease predictions. This paper discusses the use of ML in predicting heart disease, emphasizing its potential advantages and the challenges encountered. The main goal of this research is to assess the performance of various ML algorithms in predicting heart disease risk based on patient data. The dataset comprises a wide array of variables, including age, gender, blood pressure, cholesterol levels, exercise patterns, and medical history. After preprocessing these variables, we train and test several ML models, such as Logistic Regression, Random Forest, and Support Vector Machines (SVM), to evaluate their effectiveness.

Implementation of Disease Detection and Prediction of Heart using Machine Learning

Heart disease detection and prediction is an important task for entrusting whether the person is healthy or not. In recent years, there has been an increasing interest in the use of feature extraction methods for this purpose. Feature extraction methods are techniques used to identify relevant information from images and other forms of data. This information can then be used to train machine learning models to classify and predict diseases. Various feature extraction methods have been proposed for heart disease detection and prediction, including color-based features, texture-based features, and shape-based features. Color based features involve analyzing the color of blood vessels in angiography. Texture-based features involve analyzing the texture patterns within medical images, including those related to heart disease and helps to identify the texture of heart tissue in an MRI scan. Shape-based features can be used to analyze the shapes and outline of structures within medical images. Predicting heart disease using machine learning involves developing a system that can analyze various health-related data to determine the likelihood of a person having heart disease.

Wild Horse Optimizer and Support Vector Machine (SVM) Classifier Predicts The Heart Disease Converging Nature-Motivated Optimization and Machine Learning

Background: As one of the leading causes of morbidity and mortality worldwide, the diagnosis of acute cardiovascular disease (acute CVD) in emergency settings remains a significant challenge. Machine learning (ML) is artificial intelligence that allows computer programs to learn from and analyze large data sets without human intervention. As a diagnostic tool, ML presents numerous advantages, such as convenience, speed, and affordability. Methods: This research utilized a new model for predicting heart disease for feature selection(Wild Horse Optimizer, WHO) and another classification model (Support Vector Machine, SVM). In the WHO algorithm, like envisioning social behaviors of wild horses, we observe that wild horses display diverse behaviors like leading, grazing, moving, mating and chase. One odd behavior about wild horses is that young foals will break away from the herd to avoid the breed in its related herd before reaching maturity. It defines the decencies of the horses. Discussion: Multiple ML techniques were employed to train classifiers using the Cleveland heart disease dataset after conducting feature selection using WHO algorithm. The performance of these models was evaluated based on different parameters like Sensitivity, Accuracy, Specificity, and AUC. The SVM classifier model trained using the WHO approach proved better than the existing methods. In the research, we proposed a Wild Horse Optimization algorithm to optimize the features for Heart Disease prediction. After feature optimization, the optimized dataset is then given to Support Vector machine classification, which performs the classification of heart disease. After evaluating different experimental results, authors conclude that combining this feature selection algorithm and SVM as a classifier for forecasting heart disease is very effective. With the help of this approach, we can improve the early detection of heart disease and effectively manage severe heart disease. The experimental result graph shows an increased accuracy rate of the spectra feature subject.

Healthcare Monitoring-based IoT framework for heart disease detection and classification

Background: Chronic diseases like diabetes, heart disease, and cancer pose significant global health challenges, contributing to a substantial portion of worldwide mortality. Diagnosing heart disease, with its diverse signs and symptoms, remains a complex task. The growing market for connected wearable devices presents opportunities for leveraging Internet of Things (IoT) technologies in healthcare. However, diagnosing and managing heart disease effectively remains a critical concern due to its high fatality rates. Integrating IoT into the traditional healthcare system holds promise for enhancing patient outcomes, particularly for those with heart disease. Methods: A Healthcare Monitoring-based IoT framework (HM-IoT) has been developed to enable continuous monitoring of heart disease patients, eliminating the need for manual feature extraction. This framework facilitates real-time monitoring of heart failure patients through IoT-enabled devices. Data encryption using the Enhanced Encryption Standard algorithm ensures the security of patient information within the cloud platform. An Artificial Neural Network (ANN) model is employed to classify encrypted data, promptly alerting healthcare professionals to abnormal physiological conditions. Results: Evaluation in Matlab revealed impressive processing capabilities, with decryption and encryption rates of 1085 milliseconds and 1075 milliseconds, respectively. Data protection level reached 91%, while the Security Rate level attained 99%. Performance metrics, including Accuracy (98%), Sensitivity (96%), Specificity (98%), and Precision (99.4%), demonstrated the reliability of the system in detecting potential instances of heart disease. Conclusion: The Healthcare Monitoring-based IoT framework represents a significant advancement in smart healthcare solutions for heart disease management. By integrating IoT technologies with healthcare infrastructure, the framework enables real-time monitoring, enhancing prognostic capabilities and facilitating timely interventions.

Design of a Classifier model for Heart Disease Prediction using normalized graph model

Heart disease is an illness that influences enormous people worldwide. Particularly in cardiology, heart disease diagnosis and treatment need to happen quickly and precisely. Here, a machine learning-based (ML) approach is anticipated for diagnosing a cardiac disease that is both effective and accurate. The system was developed using standard feature selection algorithms for removing unnecessary and redundant features. Here, a novel normalized graph model (n – GM) is used for prediction. To address the issue of feature selection, this work considers the significant information feature selection approach. To improve classification accuracy and shorten the time it takes to process classifications, feature selection techniques are utilized. Furthermore, the hyper-parameters and learning techniques for model evaluation have been accomplished using cross-validation. The performance is evaluated with various metrics. The performance is evaluated on the features chosen via features representation. The outcomes demonstrate that the suggested (n – GM) gives 98 % accuracy for modeling an intelligent system to detect heart disease using a classifier support vector machine

Obstetric imaging practice characteristics associated with prenatal detection of critical congenital heart disease in a rural US region over 20years.

To identify clinical practice characteristics associated with the frequency of prenatal critical congenital heart disease (CCHD) detection (i.e., the number of liveborn infants with postnatally confirmed CCHD identified on prenatal sonography) over 20years in a rural setting comprised of 11 primarily low-volume obstetric hospitals and the single tertiary academic hospital to which they refer. This was a retrospective cohort study of all patients in the referral region with an initial prenatal and/or postnatal diagnosis of CCHD from 01/01/2002 to 12/31/2021. The frequency of prenatal CCHD detection at the time of an obstetric ultrasound was reported, as was the change in detection over time. Critical congenital heart disease detection was assessed as a function of cardiac lesion type, practice setting, and practice characteristics. There were 271 cases with a confirmed postnatal CCHD diagnosis, of which 49% were identified prenatally. The majority of community practices each averaged <10 CCHD cases in total over the study period. Prenatal detection at the tertiary academic hospital's obstetric ultrasound unit was 64%, compared to 22% at the combined referring community practices (p<0.001), though CCHD detection improved over time in both settings. Professional accreditation by the American Institute of Ultrasound in Medicine, image interpretation by radiology or Maternal Fetal Medicine, and use of video clips of ventricular outflow tracts were associated with improved prenatal CCHD detection. Our data demonstrate the infrequency of CCHD cases at small-volume, rural hospitals and the substantial variation in prenatal CCHD detection across practice settings. Our methods allowed for the identification of practice characteristics associated with prenatal CCHD detection.

Can we screen for heart disease in children at public health centres? A multicentre observational study of screening for heart disease with a risk of sudden death in children

Sudden cardiac death in children is a rare event, but of great social significance. Generally, it is related to heart disease with a risk of sudden cardiac death (SCD), which may occur with cardiovascular symptoms and/or electrocardiographic markers; thus, a primary care paediatrician (PCP) could detect them. Therefore, we proposed a study that assesses how to put into practice and conduct a cardiovascular assessment within the routine healthy-child check-ups at six and twelve years of age; that reflects cardiovascular signs and symptoms, as well as the electrocardiographic alterations that children with a risk of SCD in the selected population present; and that assesses the PCP’s skill at electrocardiogram (ECG) interpretation. In collaboration with PCPs, primary care nurses, and paediatric cardiologists, an observational, descriptive, multicentre, cross-sectional study was carried out in the Balearic Islands (Spain), from April 2021 to January 2022, inclusive. The PCPs gathered patient data through forms (medical record, electrocardiogram, and physical examination) and sent them to the investigator, together with the informed consent document and electrocardiogram. The investigator passed the electrocardiogram on to the paediatric cardiologists for reading, in an identical form to those the paediatricians had filled in. The variables were collected, and a descriptive analysis performed. Three paediatric cardiologists, twelve PCPs, and nine nurses from seven public health centres took part. They collected the data from 641 patients, but 233 patients did not participate (in 81.11% due to the PCP’s workload). Therefore, the study coverage was around 64%, representing the quotient of the total number of patients who participated, divided by the total number of patients who were eligible for the study. We detected 30 patients with electrocardiographic alterations compatible with SCD risk. Nine of these had been examined by a paediatric cardiologist at some time (functional murmur in 8/9), five had reported shortness of breath with exercise, and four had reported a family history of sudden death. The physical examination of all the patients whose ECG was compatible with a risk of SCD was normal. Upon analysing to what extent the ECG results of the PCP and the paediatric cardiologist agreed, the percentage of agreement in the final interpretation (normal/altered) was 91.9%, while Cohen’s kappa coefficient was 31.2% (CI 95%: 13.8–48.6%). The sensitivity of the ECG interpretation by the PCP to detect an ECG compatible with a risk of SCD was 29% and the positive predictive value 45%. Conclusions: This study lays the foundations for future SCD risk screening in children, performed by PCPs. However, previously, it would be important to optimise their training in reading and interpreting paediatric ECGs.What is Known:• In Spain at present, there is a programme in place to detect heart disease with a risk of sudden death [1], but it targets only children who are starting on or are doing a physical activity as a federated sport. Implementing such screening programmes has proven effective in several countries [2]. However, several studies showed that the incidence of sudden cardiac death is no higher in children competing in sport activities than in those who do not do any sport [3]. This poses an ethical conflict, because at present, children who do not do any federated sport are excluded from screening. According to the revised literature, so far, only in two studies did they screen the child population at schools, and in both, they successfully detected patients with heart disease associated to the risk of sudden death [4, 5]. We have found no studies where the screening of these features was included within the routine healthy-child check-ups by primary care paediatricians.What is New:• We did not know whether—in our setting, at present—the primary care paediatrician could perform a screening method within the routine healthy-child check-ups, in order to detect presumably healthy children at risk of sudden cardiac death, as they present one of the SCD risks. In this regard, we proposed our project: to assess how to put into practice and conduct a cardiovascular assessment via SCD risk screening in the healthy child population by primary care paediatricians and appraise primary care paediatricians’ skills in identifying the electrocardiographic alterations associated with SCD risk. The ultimate intention of this pilot study was to make it possible, in the future, to design and justify a study aimed at universalising cardiovascular screening and achieving a long-term decrease in sudden cardiac death events in children.

A new approach for heart disease detection using Motif transform-based CWT's time-frequency images with DenseNet deep transfer learning methods.

Electrocardiogram (ECG) signals are extensively utilized in the identification and assessment of diverse cardiac conditions, including congestive heart failure (CHF) and cardiac arrhythmias (ARR), which present potential hazards to human health. With the aim of facilitating disease diagnosis and assessment, advanced computer-aided systems are being developed to analyze ECG signals. This study proposes a state-of-the-art ECG data pattern recognition algorithm based on Continuous Wavelet Transform (CWT) as a novel signal preprocessing model. The Motif Transformation (MT) method was devised to diminish the drawbacks and limitations inherent in the CWT, such as the issue of boundary effects, limited localization in time and frequency, and overfitting conditions. This transformation technique facilitates the formation of diverse patterns (motifs) within the signals. The patterns (motifs) are constructed by comparing the amplitudes of each individual sample value in the ECG signals in terms of their largeness and smallness. In the subsequent stage, the obtained one-dimensional signals from the MT transformation were subjected to CWT to obtain scalogram images. In the last stage, the obtained scalogram images were subjected to classification using DenseNET deep transfer learning techniques. The combined approach of MT+CWT+DenseNET yielded an impressive success rate of 99.31 %.

Smart Artificial Intelligence System for Heart Disease Prediction

Heart disease playing a vital role in human life, Early detection of heart-disease we can save humans lives and it remains a leading cause of mortality worldwide, making early and accurate prediction of heart disease a critical task for improving patient outcomes. Machine learning has shown great promise in this area, with various models being developed to predict heart disease based on a range of clinical and demographic features. However, there is a growing need for more efficient machine learning models that can accurately predict heart disease while minimizing computational costs, particularly in resource-constrained settings. This research paper proposes an efficient machine learning model for heart disease prediction that combines feature selection, model optimization, and interpretability techniques to achieve accurate predictions with reduced computational complexity. The proposed model utilizes a dataset of clinical and demographic features, such as age, sex, blood pressure, cholesterol levels, and other relevant risk factors, to train a machine learning model using a large real-world dataset. The proposed efficient machine learning model is evaluated on benchmark datasets and compared with other state-of-the-art models in terms of precision, Accuracy, Recall and F1- Score. The results demonstrate the model achieved by superior prediction performance to existing models. Proposed method accuracy increased by 4.8%

Critical congenital heart disease: contemporary prenatal screening performance and outcomes in a multi-centre perinatology service

BackgroundPrenatal detection of critical congenital heart disease (CCHD) optimises perinatal decision-making and neonatal outcomes. The objective of this study was to determine the prenatal screening performance, care pathways and perinatal outcomes for prenatally and postnatally diagnosed cases of CCHD over a four-year period.Study designThis retrospective cohort study in a tertiary centre and its two affiliated secondary sites examined all cases of CCHD, including cases of pregnancy termination and in-utero fetal death, neonatal death and liveborn babies that underwent cardiac catheterization or surgery in the first six weeks of life. Prenatal and postnatal data were ascertained from the first trimester assessment for all patients diagnosed prenatally. Cases requiring intervention that were first identified in the postnatal period were included to determine prenatal detection rates. Follow-up for all cases of CCHD continued to one year of age.ResultsIn a consecutive cohort of 49,950 pregnancies in a 4-year period 01/2019 to 12/2022, a prenatal diagnosis of CCHD was made in 96 cases, yielding a prevalence of 1.9 per 1000 births. The prenatal detection for right duct-dependant heart pathology and congenital heart block was 100%, 85% for left duct-dependant pathology and 93% for transposition of the great arteries (TGA). In the prenatally diagnosed group, 37% of cases were complicated by extracardiac structural abnormalities, a genetic diagnosis or both. All cases of prenatal detection were identified in the context of routine anatomy screening rather than specialist Fetal Cardiac screening services. Almost half of all pregnancies complicated by CCHD did not undergo neonatal cardiac intervention, by virtue of parental choice determined either prenatally or after birth. An additional eight babies were diagnosed with CCHD in the neonatal period, such that the prenatal detection rate for CCHD was 92% (96/104, 95% CI = 84%-96%). Survival at 1-year for infants deemed suitable for CCHD surgery was 85%.ConclusionIn a large unselected population, optimal rates of prenatal detection of critical congenital heart disease can be achieved by a protocolised approach to mid-trimester fetal anatomy ultrasound, underpinned by a programme of sonographer education and training. The cardiac abnormalities most likely to evade prenatal detection are left-sided obstructive lesions.

Enhancing heart disease detection and classification using fuzzy logic and metaheuristic algorithms in a blockchain assisted healthcare environment

Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment.

Enhancing heart disease detection in IoT: optimizing long short-term memory with enhanced jellyfish optimization

Enhancing heart disease detection in IoT: optimizing long short-term memory with enhanced jellyfish optimization

Artificial intelligence framework for heart disease classification from audio signals

As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model’s performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.

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