Early Detection Of Cardiovascular Disease Research Articles

Heart sound classification algorithm based on bispectral feature extraction and convolutional neural networks

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.

Harnessing AI for Early Detection of Cardiovascular Diseases: Insights from Predictive Models Using Patient Data

Cardiovascular diseases (CVDs) continue to be the leading cause of death in the world, taking millions of lives every year. Early detection and treatment of these diseases are critical in preventing deaths and illnesses. Currently, traditional diagnostic methods like routine clinical examinations and other standard risk assessment tools fail to detect CVDs in early on set stages, posing numerous limitations for preventable interventions. This work explores incorporating artificial intelligence (AI) to catalyze early CVD diagnosis using AI models for patient data including electrocardiogram (ECG), wearable-generated metrics, and medical history. The research showcased the ability of AI to predict with high accuracy when CVD would start using machine learning methods such as Random Forest, Support Vector Machines (SVM) and Neural Networks. The powerful AI models were carefully constructed by training and testing them on an extensive dataset that contained measurements of diverse patient characteristics including many other readouts related to cardiovascular risk. Compared to traditional methods, the models outperformed, and Neural Networks had an accuracy of 92% in identifying high-risk patients. A hallmark of this work is that AI identifies subtle patterns and relationships within patient data ones that conventional approaches might not find. These AI-based predictive models might soon become part of the routine clinical evaluation for cardiovascular disease, providing a personalized and early health intervention to empower your cardiovascular care. Timely identification of these high-risk patients could allow for targeted interventions, leading to improved health and reductions in healthcare spending. Despite the promising potential that AI holds in healthcare, it also has brought up issues of data privacy, ethical use and the need for extensive clinical validation. This paper suggests a predictive strategy could be implemented using AI algorithms to lead early detection and management of CVD, which opens an opportunity for individualized care accompanied by big data era. Future studies should focus on improving such models with more diverse and larger datasets as well as overcoming logistical difficulties encountered in integrating AI into clinical workflows.

Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems

Abstract Electrocardiogram (ECG) recognition systems now play a leading role in the early detection of cardiovascular diseases. However, the explanation of judgments made by deep learning models in these systems is prominent for clinical acceptance. This article reveals the effect of transfer learning in ECG recognition systems on decision precision. This article investigated the role of transfer learning in ECG image classification using a customized convolutional neural network (CNN) with and without a VGG16 architecture. The customized CNN model with the VGG16 achieved a good test accuracy of 98.40%. Gradient-weighted class activation mapping (Grad-CAM), for this model, gave the wrong information because it focused on parts of the ECG that were not important for making decisions instead of features necessary for clinical diagnosis, like the P wave, QRS complex, and T wave. A proposed model that only used customized CNN layers and did not use transfer learning performed 99.08% on tests gave correct Grad-CAM explanations and correctly identified the influencing areas of decision-making in the ECG image. Because of these results, it seems that transfer learning might provide good performance metrics, but it might also make things harder to understand, which could make it harder for deep learning models that use ECG recognition to be reliable for diagnosis. This article concludes with a call for careful consideration when using transfer learning in the medical field, as model explanations resulting from such learning may not be appropriate when it comes to domain-specific interpretations.

Blood-based HYAL2 methylation as a potential marker for the preclinical detection of coronary heart disease and stroke

BackgroundCoronary heart disease (CHD) and stroke have become the leading cause of premature mortality and morbidity worldwide. Therefore, sensitive and accurate biomarkers for early detection of CHD and stroke are urgently needed for effective prevention and treatment. We aim to investigate the association between blood-based HYAL2 methylation and the risk of CHD and stroke in Chinese population.MethodsIn a prospective nested case–control study comprising 171 CHD cases, 139 stroke cases, who developed the diseases after recruitment and 356 controls who remained healthy during the 2.5 years of follow-up time, the methylation level of HYAL2 in the peripheral blood was quantified using mass spectrometry, and the association was calculated by logistic regression adjusted for covariant.ResultsSignificant association between HYAL2 methylation in the peripheral blood and increased risk of preclinical CHD and stroke were identified [odds ratios (ORs) per − 10% methylation: 1.35–1.64, p ≤ 0.045 for HYAL2_CpG_1, HYAL2_CpG_2 and HYAL2_CpG_3 in CHD; ORs per − 10% methylation: 0.76–1.64, p ≤ 0.033 for HYAL2_CpG_2 and HYAL2_CpG_4 in stroke]. The association in CHD was further enhanced by female gender, younger age (< 70 years old), without the history of hypertension and cancer. The combination of four HYAL2 methylation sites showed an effective discrimination of CHD and stroke cases without hypertension from controls [area under curve (AUC) = 0.78 and 0.75, respectively].ConclusionsThis study presents a strong association of altered HYAL2 methylation in peripheral blood with preclinical CHD and stroke, providing a novel biomarker for risk assessment and early detection of cardiovascular diseases.

Heart Sound Processing for Early Diagnostic of Heart Abnormalities using Support Vector Machine

This paper addresses the critical issue of cardiovascular disease (CVD), the leading cause of global mortality, emphasizing the imperative for effective and early detection to mitigate CVD-related deaths. The research problem underscores the urgency of developing advanced diagnostic tools to identify heart abnormalities promptly. The primary objective is to create a Support Vector Machine (SVM) algorithm for accurate classification of different heart conditions, namely Normal heart, Mitral Stenosis, and Mitral Regurgitation. To achieve this objective, the study utilizes a dataset of heart sounds available online using a 10-fold cross-validation method. The focus is on evaluating the efficacy of various kernel functions within the SVM framework for heart sound classification. The findings demonstrate that the linear kernel exhibits superior accuracy and robustness in effectively classifying heart conditions. Notably, the proposed classification method attains an impressive 96% accuracy, highlighting its potential as a reliable tool for early detection of cardiovascular diseases. This research contributes to the ongoing efforts to enhance diagnostic capabilities and ultimately reduce the global burden of CVD-related fatalities.

A one-dimensional convolutional neural network-based deep learning approach for predicting cardiovascular diseases

Early detection of cardiovascular diseases (CVDs) is crucial for managing cardiovascular diseases and improving patient outcomes. Deep neural networks have the potential to reduce the reliance on costly and time-consuming clinical tests, leading to cost savings for patients and healthcare systems. This study proposes the development of specialized convolutional neural networks for the automated selection of essential variables, employing various preprocessing procedures. It evaluates the approach using the UCI repository heart disease dataset, focusing on early-stage heart disease identification to enhance early prediction and intervention for CVD. To address the challenge of achieving higher accuracy, we introduce an approach using one-dimensional convolutional neural networks, incorporating extensive testing to optimize the network architecture and enhance predictive performance. Additionally, recognizing the impact of features on accuracy, a comprehensive data analysis was performed. Through a meticulous selection process, we identified and utilized key features that significantly influenced the accuracy of our model, contributing to more reliable predictions. Finally, cross-validation techniques were implemented to precisely evaluate the efficacy of our work. Numerous experiments were conducted to demonstrate the relevance of our research. The prediction accuracy was found to be 99.95% when employing a train–test approach, while it was approximately 98.53% when employing K-Fold cross-validation. In comparison to existing literature, our approach outperforms a recent best study that proposed a Catboost model, achieving an F1-score of about 92.3% and an average accuracy of 90.94%. This signifies a substantial improvement in predictive performance, with a percentage improvement of approximately 9.90% compared to the Catboost model.

Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method.

Cardiovascular diseases (CVDs) are the leading global cause of mortality, necessitating advanced diagnostic tools for early detection. The electrocardiogram (ECG) is pivotal in diagnosing cardiac abnormalities due to its non-invasive nature. This study aims to propose a novel approach for ECG signal classification, addressing the challenges posed by the complexity of ECG signals associated with various diseases. Our method integrates Discrete Wavelet Transform (DWT) for feature extraction, capturing salient features of cardiovascular diseases. Subsequently, the gcForest model is employed for efficient classification. The approach is tested on the MIT-BIH Arrhythmia Database. The proposed method demonstrates promising results on the MIT-BIH Arrhythmia Database, achieving a test accuracy of 98.55%, recall of 98.48%, precision of 98.44%, and an F1 score of 98.46%. Additionally, the model exhibits robustness and low sensitivity to hyper-parameters. The combined use of DWT and the gcForest model proves effective in ECG signal classification, showcasing high accuracy and reliability. This approach holds potential for improving early detection of cardiovascular diseases, contributing to enhanced cardiac healthcare.

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML with chi-square feature selection to detect CVD early. Our approach involved applying multiple ML classifiers, including naïve Bayes, random forest, logistic regression (LR), and k-nearest neighbor. These classifiers were evaluated through metrics including accuracy, specificity, sensitivity, F1-score, confusion matrix, and area under the curve (AUC). We created an ensemble model by combining predictions from the different ML classifiers through a voting mechanism, whose performance was then measured against individual classifiers. Furthermore, we applied chi-square feature selection method to the 303 records across 13 clinical features in the Cleveland cardiac disease dataset to identify the 5 most important features. This approach improved the overall accuracy of our ensemble model and reduced the computational load considerably by more than 50%. Demonstrating superior effectiveness, our voting ensemble model achieved a remarkable accuracy of 92.11%, representing an average improvement of 2.95% over the single highest classifier (LR). These results indicate the ensemble method as a viable and practical approach to improve the accuracy of CVD prediction.

Early Detection of Cardiovascular Disease with Different Machine Learning Approaches

With the increase in mortality rate around the world in recent years, cardiovascular diseases (CVD) have swiftly become a leading cause of morbidity, and therefore there arises a need for early diagnosis of disease to ensure effective treatment. With machine learning emerging as a promising tool for the detection, this study aims to propose and compare various algorithms for the detection of CVD via several evaluation metrics including accuracy, precision, F1 score, and recall. ML has the ability and potential to improve CVD prediction, detection, and treatment by analysis of patient information and identification of patterns that may be difficult for humans to interpret and detect. Several state-of-the-art ML and DL models such as Decision Tree, XGBoost, KNN, and ANN were employed. The results of these models reflect the potential of Machine Learning in the detection of CVD detection and subsequently highlight the need for their integration into clinical practice along with the suggestion of the development of robust and accurate models to improve the predictions. This integration, however, significantly helps in the reduction of the burden of CVD on healthcare systems.

Abstract A022: Detecting cardiovascular disease risk in early-stage endometrial cancer survivors: preliminary evidence of pulse wave velocity and social vulnerability index

Abstract BACKGROUND: Endometrial cancer survivors (ECS) experience high rates of cardiovascular disease (CVD) likely due to lifestyle and social-environmental factors, including high obesity rates up to 60%. Early detection of CVD is crucial in order to intervene; however, strategies to identify ECS most at risk for CVD are currently lacking. The gold-standard methodology to non-invasively measure CVD is pulse wave velocity (PWV), and one way to identify social-environmental susceptibility is the Centers for Disease Control social vulnerability index (SVI). PURPOSE: Therefore, the purpose of this study is to investigate (1) CVD risk in stage 1 ECS using PWV and (2) the social-environmental influence on CVD risk using SVI. METHODS: Stage 1 ECS who were 1-12 months post-treatment, with a BMI ≥ 25.0 kg/m2, age 50-80 years, and English speaking were recruited in the gynecology-oncology clinic. Oscillometric PWV was measured in a supine position via Mobil-O-Graph. Body composition [body fat percentage (%)] was measured via Bioelectrical Impedance Analysis. Participant zip code was used to determine SVI of their county of residence. SVI is defined as low-medium (0.00-0.49), medium-high (0.50-0.69), and high (0.70-1.00). Linear regression was used to compare PWV and SVI. RESULTS: Seventeen ECS (65% White, 25% Black, 5% Asian, 5% American Indian, 64 ± 7 years old, 6 ± 4 months post-treatment, 44 ± 6% body fat) participated in the study. Participants resided in counties with low-medium (18%), medium-high (24%), and high (58%) SVI and PWV was measured as 8.2 ± 0.8 m/s, 9.1 ± 0.8 m/s, and 9.7 ± 1.0 m/s respectively. The overall regression was not statistically significant (R=0.38, R2=0.14, F=1.15, p=0.34). DISCUSSION: Overall, PWV was elevated in ECS residing in counties with medium-high and high SVI categories compared to normative data. PWV can be used to non-invasively measure CVD risk in clinic in early-stage ECS. Despite failing to reach statistical significance, a 1.0 m/s change is considered to be a clinically meaningful difference in PWV. Therefore, ECS residing in counties with high SVI may be at greater CVD risk compared to those living in less vulnerable communities. Further research is needed, including a larger sample size, to identify successful interventions to reduce CVD in ECS most at risk. Citation Format: Lauren C. Bates-Fraser, Victoria Bae-Jump, Lee Stoner, Erik D. Hanson. Detecting cardiovascular disease risk in early-stage endometrial cancer survivors: preliminary evidence of pulse wave velocity and social vulnerability index [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr A022.

Multi-Feature Decision Fusion Network for Heart Sound Abnormality Detection and Classification.

The heart sound reflects the movement status of the cardiovascular system and contains the early pathological information of cardiovascular diseases. Automatic heart sound diagnosis plays an essential role in the early detection of cardiovascular diseases. In this study, we aim to develop a novel end-to-end heart sound abnormality detection and classification method, which can be adapted to different heart sound diagnosis tasks. Specifically, we developed a Multi-feature Decision Fusion Network (MDFNet) composed of a Multi-dimensional Feature Extraction (MFE) module and a Multi-dimensional Decision Fusion (MDF) module. The MFE module extracted spatial features, multi-level temporal features and spatial-temporal fusion features to learn heart sound characteristics from multiple perspectives. Through deep supervision and decision fusion, the MDF module made the multi-dimensional features extracted by the MFE module more discriminative, and fused the decision results of multi-dimensional features to integrate complementary information. Furthermore, attention modules were embedded in the MDFNet to emphasize the fundamental heart sounds containing effective feature information. Finally, we proposed an efficient data augmentation method to circumvent the diagnosis performance degradation caused by the lack of cardiac cycle segmentation in other end-to-end methods. The developed method achieved an overall accuracy of 94.44% and a F1-score of 86.90% on the binary classification task and a F1-score of 99.30% on the five-classification task. Our method outperformed other state-of-the-art methods and had good clinical application prospects.

Early Detection of Cardiovascular Disease Utilizing Machine Learning Techniques: Evaluating the Predictive Capabilities of Seven Algorithms

Heart disease is the leading cause of death in developed countries, as it causes many deaths annually. Despite the availability of effective treatments, heart disease remains a significant challenge to public health, so early detection is essential in enhancing patient outcomes and reducing mortality. Artificial intelligence seeks to help physicians make the right decisions about a patient's health condition. In this regard, the authors decided to utilize machine learning techniques (k-nearest neighbor, decision tree, linear regression, support vector machine, naïve bayes, multilayer perceptron, random forest) to contribute to the classification of the heart disease dataset, where it is determined whether a person is suffering or not. After that, the execution of all techniques will be measured, and the accuracy of each technique will be compared to determine the most suitable performer. The public dataset is organized from the UC Irvine machine learning repository and have significantly different characteristics. The dataset will be divided such that 80% of the data is designated for training and 20% is designated for testing. This article concluded that the adequate performance is for the multilayer perceptron technique, as it gained an accuracy of more than 88%, while the poor performance is for the decision tree technique, as it gained an accuracy of more than 79%.

Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments

Cardiovascular diseases (CVDs) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appropriate counseling and medication, which can effectively manage the condition and improve patient outcomes. This study introduces an innovative ontology-based model for the diagnosis of CVD, aimed at improving decision support systems in healthcare. We developed a database model inspired by ontology principles, tailored for the efficient processing and analysis of CVD-related data. Our model’s effectiveness is demonstrated through its integration into a web application, showcasing significant improvements in diagnostic accuracy and utility in resource-limited settings. Our findings indicate a promising direction for the application of artificial intelligence (AI) in early CVD detection and management, offering a scalable solution to healthcare challenges in diverse environments.

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images

ABSTRACT Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research’s culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.

A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease

Cardiovascular disease (CVD) remains a leading cause of death globally, presenting significant challenges in early detection and treatment. The complexity of CVD arises from its multifaceted nature, influenced by a combination of genetic, environmental, and lifestyle factors. Traditional diagnostic approaches often struggle to effectively integrate and interpret the heterogeneous data associated with CVD. Addressing this challenge, we introduce a novel Attention-Based Cross-Modal (ABCM) transfer learning framework. This framework innovatively merges diverse data types, including clinical records, medical imagery, and genetic information, through an attention-driven mechanism. This mechanism adeptly identifies and focuses on the most pertinent attributes from each data source, thereby enhancing the model’s ability to discern intricate interrelationships among various data types. Our extensive testing and validation demonstrate that the ABCM framework significantly surpasses traditional single-source models and other advanced multi-source methods in predicting CVD. Specifically, our approach achieves an accuracy of 93.5%, precision of 92.0%, recall of 94.5%, and an impressive area under the curve (AUC) of 97.2%. These results not only underscore the superior predictive capability of our model but also highlight its potential in offering more accurate and early detection of CVD. The integration of cross-modal data through attention-based mechanisms provides a deeper understanding of the disease, paving the way for more informed clinical decision-making and personalized patient care.

Abnormal Heart Sound Detection using Time-Frequency Analysis and Machine Learning Techniques

Phonocardiogram (PCG) signals contains valuable information pertaining to heart valve functionality, rendering them potentially useful for early detection of cardiovascular diseases. Automated classification of heart sounds harbors great promise for identifying cardiac pathologies. This paper introduces a novel automated approach to classify normal and abnormal heart sounds. Our methodology involves partitioning heart sounds into four segments: S1, S2, systolic, and diastolic, followed by extraction of time–frequency and time-statistical features. Prior to data classification, we employ two techniques - particle Swarm optimization (PSO) and Sequential Forward Feature Selection (SFFS) - for efficient feature selection. We assess the performance of the proposed method on the Physionet Challenge 2016 database, utilizing the 10-fold cross-validation method. To address the issue of dataset imbalance, we apply the synthetic minority over-sampling technique (SMOTE) to create balanced datasets. Our approach surpasses existing methods in the literature, as evidenced by its superior accuracy, sensitivity, and specificity metrics. Specifically, our method achieves an accuracy of 98.03%, a sensitivity of 97.64%, and a specificity of 98.43% in distinguishing normal from abnormal heart sounds on the Physionet database. These findings outperform the results obtained by previously established methods evaluated on the Physionet 2016 challenge database.

Use of computed tomography coronary calcium score for prediction of cardiovascular events in cancer patients: a retrospective cohort analysis

BackgroundCT- coronary calcium score, is one of the most studied and widely available modalities in cardiovascular medicine. Coronary artery calcium score (CACS) is an established predictor of coronary artery disease. The ‘standard of care’ diagnostic modality to measure CACS is ECG-gated Cardiac Multi-Detector Computed Tomography. There is convincing evidence of a strong association between CACS and major cardiovascular (CV) events in asymptomatic individuals. Cancer patients (C) may have a higher risk for CV disease than non-cancer patients (NC) related not only to cancer treatments but also to shared biological factors and pathways. Thus, identifying tools for early detection of CV disease in this population is of utmost importance.MethodsA retrospective cohort analysis was performed with patients from Cleveland Clinic Florida and Ohio who had CACS from 2017 to 2021. Patients who had cancer diagnosis prior to CACS were matched to NC for age and sex. CV events after their index CACS events were compared between C and NC, and matched control and propensity analysis were conducted.ResultsTen thousand seven hundred forty-two patients had CACS; 703 cancer patients had CACS and were eligible. Extensive CACS (> 400) were significantly higher in cancer, 94 (13.37%) vs non-cancer patients, 76 (10.83%), P = 0.011. Furthermore, after propensity matched analysis, CACS > 400 was 14.8% in C vs 9.6% in NC, P = < 0.05. CV events were similar in both cohorts (p = NS), despite less CV risk factors in cancer patients (P = < 0.05). For the combined moderate (101–400) & extensive (> 400) CACS, the prevalence of stroke and peripheral arterial disease, a marker of systemic atherosclerosis, was significantly higher in patients with cancer (P < 0.01).ConclusionsDespite having fewer CV risk factors in our study, similar CACS in cancer patients are suggestive of a higher prevalence of CV disease independent of traditional risk factors. High CACS and the overall prevalence of vascular events were more frequent in patients with cancer. Higher prevalence of peripheral arterial disease and cerebrovascular accident further suggests the increased atherosclerotic burden in C.

The Comparative Early Prediction Model for Cardiovascular Disease Using Machine Learning

Cardiovascular disease (CVD) is a leading cause of death and a major contributor to disability. Early detection of cardiovascular disease using ANFIS has the potential to reduce costs and simplify treatment. This study aims to develop a prediction model using ANFIS (Adaptive Neuro-Fuzzy Inference System) for early detection of cardiovascular disease. The dataset used consists of 500 data with 12 features, including various risk factors such as blood sugar levels, cholesterol, uric acid, systolic blood pressure, diastolic blood pressure, body mass index (BMI), age, smoking habits, lifestyle, genetic factors, and gender, and one label feature. This study compares cardiovascular disease prediction models using machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and ANFIS. The development of the KNN algorithm involves the value of K=5 with the Euclidian distance measure. The SVM algorithm used a kernel cache of 200 and a convergence epsilon of 0.001. The ANFIS model was built using 500 data sets divided into training (70%) and testing (30%) data, with learning rate variations of 0.01, 0.05, 0.1, 0.2, and 0.5. The results of testing the early detection model show for SVM, the accuracy value is 0.760, the precision value is 0.839, and the recall value is 0.671. For the KNN model, the accuracy value is 0.758, the precision value is 0.768, and the recall value is 0.771. As for the ANFIS model, the accuracy value reaches 0.989, precision value 0.996, and recall value 0.988. The model using ANFIS has the highest performance. Further study of the model using ANFIS with learning rate variations shows that a learning rate of 0.1 provides the most optimal performance.

Ventricular Ectopic beats detection based wavelet scattering network and ensemble bagged trees for smart medical systems

This paper introduces an automated method for classifying electrocardiograms (ECG) with the aim of enhancing both accuracy and speed in interpreting ECG results, thus contributing to the early detection of cardiovascular diseases (CVD). The model in this study employs a deep feature extraction technique combined with Ensemble Bagged Trees (EBT) to identify Ventricular Ectopic Beats (VEB). Data is sourced from the MIT-BIH Arrhythmia database, and model performance is assessed through ten-fold cross-validation. On the validation dataset, the proposed model achieves an accuracy of 98.24% and an F1 score of 97.66%. When independently tested on the MIT-BIH dataset, it demonstrates an accuracy of 98.31% and an F1 score of 89.22%. These outcomes highlight the model's ability to attain exceptional accuracy and F1 scores on both validation and testing datasets.

Artificial intelligence in cardiovascular medicine: An updated review of the literature.

Screening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.