Early Diagnosis Of Heart Disease Research Articles

Children’s heart and prematurity: a current view of the problem

Improved survival of children born with low, extremely low and very low body weight in modern conditions due to increased nursing capabilities, optimization of treatment and increased efficiency of resuscitation measures has led to the need for greater understanding of the importance of assessing the cardiovascular system’s state beyond the neonatal period. With approximately 10% of infants worldwide being born preterm, there is an increasing need for further research into optimal regimens, lifestyle and clinical interventions that can benefit and modify cardiovascular morphology and function in this growing population. Modern theoretical postulates on the physiology and pathophysiology of the child’s heart include ideas about the key process of cardiac biomechanics — diastole, during which the earliest disturbances, that precede the formation of systolic dysfunction, occur. Assessment of the pumping properties of the left ventricle in systole and diastole is a fundamental methodological technique for an in-depth understanding of the pathophysiological mechanisms of cardiovascular system’s emerging diseases, their early diagnosis and assessment of the complex therapy’s effectiveness. However, despite the widespread use of standard echocardiography to assess systolic and diastolic function, its traditional parameters have limitations in terms of diagnostic accuracy and effectiveness in practice. Expanding knowledge about new pathogenetic mechanisms of the cardiac dysfunction formation in conditions of prematurity at the current stage of cardiology development using the “trace spot” technology (two-dimensional speckle tracking echocardiography) will be very useful for scientists studying the mechanics formation of the child’s heart after premature birth, and for doctors of various specialties in early diagnosis of heart diseases.

Clinical Support System for Cardiovascular Disease Forecasting Using ECG

INTRODUCTION: Heart failure is a chronic condition that affects many people worldwide. Regrettably, it is now the biggest cause of mortality globally, and it is becoming more common. Before a cardiac event, early diagnosis of heart disease is challenging. Although healthcare institutions like hospitals and clinics have access to a wealth of heart disease data, it is rarely used to uncover underlying trends.
 OBJECTIVES: Algorithms for machine learning (ML) can turn this medical data into insightful information. These methods are used to create decision support systems (DSS) that can gain knowledge from the past and advance. It is essential to use an effective ML-based technique to identify early heart failure and take preventive action to address this worldwide issue. Accurately identifying heart illness is our main goal in this study.
 METHODS: For this work, we benchmark different datasets on heart illness, and we use feature engineering approaches to pick the most pertinent qualities for improved performance. Additionally, we assess nine ML methods using critical parameters including precision, f-measure, sensitivity, specificity, and accuracy.
 RESULTS: Iterative tests are carried out to evaluate the efficacy of different algorithms. With a flawless cross-validation accuracy score of 99.51% and 100% in all other metrics, our suggested Decision Tree approach performs better than other ML models and cutting-edge studies.
 CONCLUSION: Each methodology used in our study is validated using cross-validation techniques. The medical community benefits greatly from this research study.

Stacking ensemble based hyperparameters to diagnosing of heart disease: Future works

BackgroundHeart disease is one of the most recurrent and worrying health problems today, due to its multiple complications, including: stroke, cardiac arrest, retinopathy, etc. ObjectivePropose a method and 4 Stacking models based on hyperparameters to diagnose heart disease. In addition, a web interface was developed with the best model proposed in this study. MethodsFirst, the dataset used was from the Heart Disease Cleveland ICU, which was 918 patient records and 12 attributes. Therefore, the paper was composed of the following stages: Cleaning and Pre-processing; Describe data; Training and testing data; Cross validation; Calibration of models; and modelling and evaluation, also compare the different techniques proposed to predict heart disease using Stacking ensemble based on hyperparameters taking into account the performance evaluation parameters. ResultsStacking 1 (Logistic regression) with oversampling and AdaBoost-SVM with hyperparameter in the test obtained higher Accuracy (88.24%), and ROC Curve (92.00%), while too Stacking 1 (Logistic regression) with oversampling reached a better Precision (88.54%), but the AdaBoost-SVM algorithm using hyperparameter achieved a high value of Sensitivity (88.14%) and F1-Score (88.19%). ConclusionsImplementing 4 stacking models based on hyperparameters, it helps to make an early diagnosis of heart disease and greater precision, and decrease the quantity of deceases caused by it. Therefore, by using the combined method, an improvement in heart disease prediction was observed, surpassing the performance of the independent algorithms used.

Identification of miRNA Targeting Genes Involved in Hyperlipidemia as a Therapeutic Strategy for Cardiovascular Diseases

Hyperlipidemia is a common metabolic disorder and one of the independent risk factors for the onset of cardiovascular diseases (CVDs). CVDs are the leading cause of mortality in diabetic patients globally. In recent years, compelling evidence suggested that aberrant miRNA expression has been implicated in the pathophysiology of many diseases and is considered a prognostic biomarker for early diagnosis of heart diseases. Therefore, analysis of hyperlipidemia-related miRNAs is a potential approach to selectively inhibit or activate specific genes for therapeutic purposes. We identified and screened a set of hyperlipidemia and CVDs associated genes by the genetic testing registry (GTR) of NCBI. Furthermore, we predicted highly conserved putative miRNAs that target genes through binding at 8mer units by using bioinformatics repositories Targetscan, miRBase, and miRanda. Insilico miRNAs profiling of shortlisted genes revealed the target miRNAs; ACTN4 (miR-153-3p), ADCY3 (miR-3681-3p/ 216a-3p/ 128-3p/ 873-5p.1/ 92b-3p), LEP (miR-532-5p/ 29b-3p/ 29a-3p/ 29c-3p/ 125b-5p/ 125a-5p. GHR (miR-151a-3p/ 19a-3p/ 19b-3p/ 143-3p/15b-5p/ 15a-5p/ 195-5p), LPL(miR-27a-3p/ 27b-3p/ 29b-3p/, 29a-3p/ 29c-3p) LRP6 (miR-183-5p.1/ 124-3p.2/ 506-3p/ 3681-3p/ 128-3p/ 216a-3p/ 27a-3p/ 27b-3p/ 543/ 140-3p.1PCSK9 (miR-202-5p/155-5p), USF1 (miR-338-3p/ 296-5p), TNFRSF1B (miR-7/4458miR-4500), HMCN1(miR-29c-3p/ 29b-3p/ 29a-3p/ 199a-5p/ 199b-5p), KDM3A(miR-140-3p.1/ 30a-5p/ 30d-5p/ 30c-5p/ 30b-5p/ 30e-5p/ 542-3p), SEC61A1(miR-218-5p/ 331-3p), ADD1(miR-27b-3p/ 27a-3p/ 299-3p/ 448), PPARGC1B(miR-30e-5p/ 30a-5p/ 30d-5p/ 30c-5p/ 30b-5p/ 449b-5p/ 449a/ 34c-5p/ 30/ 27a-3p/ 27b-3p/ SH2B1(miR-142-3p.1/ 136-5p), PPARA (miR-19a-3p/ 19b-3p/ 9-5p/ 101-3p.2/ 4262/ 181c-5p/ 181d-5p/ 181b-5p/ 181a-5p/ 506-3p/ 124-3p.2). Conclusively, our predicted microRNAs may provide better insights towards miRNA-based treatment strategies against CVDs.

Relaxin as a biological marker and therapeutic target in heart failure

An important task of modern cardiology is the search and study of new cardiovascular biological markers that can help in the early diagnosis of heart diseases, serve as a tool to assess the effectiveness of treatment, and act as a prognostic marker and risk stratification criterion. This literature review aimed to consider relaxin (RLN) as a new diagnostic and prognostic cardiovascular biological marker. RLN is a natural peptide hormone with a molecular weight of approximately 6000 daltons. RLN2 is the main circulating form of RLN in the blood. Although human RLN2 was originally discovered as a hormone mainly secreted by the corpus luteum of the ovary, it is also synthesized in various tissues in non-pregnant women and men and is considered one of the most pleiotropic hormones of the human body, which perform various activities beyond reproduction.
 The availability of serelaxin (a recombinant molecule identical to human RLN2) has made it possible to study the effects of RLN2 on the cardiovascular system, kidneys, liver, and brain and to evaluate it in several randomized placebo-controlled clinical trials. RLN2 exerts many cardioprotective effects on the heart and vessels and has been proposed as a therapeutic target for cardiovascular diseases such as heart failure, atrial fibrillation, coronary heart disease, myocardial infarction, and arterial hypertension. Through the activation of its cognate receptor and subsequent flow of several molecular signaling pathways in the cardiovascular system, RLN2 can induce vasodilation and angiogenesis, increase arterial compliance and cardiac output, reduce vascular resistance, exert antifibrotic effects through regulation and remodeling of extracellular matrix turnover, reduce inflammation and apoptosis, inhibit oxidative stress, induce chronotropic and inotropic effects, and inhibit ventricular and atrial ectopic activity.
 Further studies are needed to demonstrate the potential use of RLN as an additional laboratory tool for diagnosis, risk stratification, and prediction of cardiovascular events in patients with heart failure. The effects of relaxin on morbidity and mortality in patients with heart failure have yet to be evaluated in more detail.

A Novel Detector of Atypical Beats for Early Diagnosis of Heart Diseases Based on the Stacked Beats Representation of 12-lead ECG

We have developed and present in this work a series of algorithms that present a long-duration electrocardiogram (ECG) in a compact form of stacked beats, extracting and visualizing the basic features and facilitating the tedious and time-consuming process of ECG analysis for cardiologists. The expert system based on this representation provides detection of atypical heartbeats, precursors of cardiovascular disease, and their locations in each of the 12 leads. This system was extensively tested with two public databases, MIT-BIH arrhythmia database and China Physiological Signal Challenge (CPSC2018), showing its rapid ECG processing and high efficiency in detecting abnormalities in beat morphology. In particular, tests for atypical beats based on the CPSC2018 database revealed that the set of ECGs marked as normal contains a considerable number of leads with atypical beats. The system is used as a classifier into two classes, normal beats, and atypical beats, the latter being the precursors or indicators of cardiovascular diseases (CVD). It is considered potentially useful for routine studies in groups at high risk of CVD in early stages, as a preventive medicine tool in the public health area. The system allows an intervention of a cardiologist in the intermediate stages of ECG analysis to corroborate the diagnosis in ambiguous cases.

An on-device machine learning assisted system for unobtrusive cardiac auscultation

This paper uses on-device machine learning to present a microcontroller-based cardiac auscultation system for heart sound classification. Real-time analysis of heart sounds for the identification of abnormalities can prove important in early diagnosis of heart disease. However, this process of cardiac auscultation faces two important challenges, one of which is the form factor of the device that is being used for the auscultation/analysis and the other being poor classification performance due to the presence of environment noise in the recorded heart sound. This paper, therefore, develops a signal processing pipeline to extract three distinct two-dimensional features from a recorded heart sound and numerically analyses the effect of environment noise on them. Henceforth, a Convolutional Neural Network (CNN) based classification layer is proposed to perform real-time analysis on heart sounds. The classifier is seen to achieve a peak accuracy of 91.2%, a precision of 91.2%, an F-1 score of 90.2%, with Youden’s index of 0.847 and discriminant power of 2.980. This classification layer then forms the basis of development for a portable device that can perform real-time heart sound classification while consuming around 68.34 mW of power and is also impervious to environmental distortions.

Decision Support System for early Diagnosis of Heart Diseases

Heart disease remains a leading cause of death worldwide, emphasizing the critical need for early diagnosis to enable effective treatment and management. Decision support systems (DSS) have the potential to significantly enhance the accuracy and efficiency of heart disease diagnosis. An advanced DSS designed for early diagnosis can provide healthcare professionals with essential information, expert advice, and treatment recommendations based on comprehensive patient data. By gathering information from various sources including medical history, risk factors, and test results, the DSS analyzes and processes the data using established diagnostic criteria and the latest medical research. The DSS then generates a diagnosis or a list of possible diagnoses, along with appropriate treatment recommendations, empowering healthcare professionals to make well-informed decisions and deliver more effective patient care. Incorporating machine learning algorithms into the DSS can further enhance its accuracy and efficiency. By training the DSS on extensive patient datasets, it can identify patterns and make predictions based on new patient data, ultimately leading to improved decision-making and better patient outcomes. In conclusion, the utilization of a DSS for heart disease diagnosis holds the potential to revolutionize the field by providing healthcare professionals with vital information, expert guidance, and treatment recommendations, thereby enhancing the accuracy, efficiency, and overall outcomes of heart disease diagnosis and management.

New Metric to Evaluate Cardiac Anisotropic Mechanics by Directional High-Frequency Ultrasound-Based Transverse Wave Elastography

The evaluation of cardiac anisotropic mechanics is important in the diagnosis of heart disease. However, other representative ultrasound imaging-based metrics, which are capable of quantitatively evaluating anisotropic cardiac mechanics, are insufficient for accurately diagnosing heart disease due to the influence of viscosity and geometry of cardiac tissues. In this study, we propose a new ultrasound imaging-based metric, maximum cosine similarity, for quantifying anisotropic mechanics of cardiac tissues by evaluating the periodicity of the transverse wave speeds depending on the measurement directions using ultrasound imaging. We developed a high-frequency ultrasound-based directional transverse wave imaging system to measure the transverse wave speed in multiple directions. The ultrasound imaging-based metric was validated by performing experiments on forty rats randomly assigned to four groups; three doxorubicin treatment groups received 10 mg/kg, 15 mg/kg, or 20 mg/kg doxorubicin, while the control group received 0.2 ml/kg saline. In each heart sample, the developed ultrasound imaging system allowed measuring transverse wave speeds in multiple directions, and the new metric was then calculated from 3D ultrasound transverse wave images to evaluate the degree of anisotropic mechanics of the heart sample. The results of the metric were compared with histopathological changes for validation. A decrease in the maximum cosine similarity value was observed in the doxorubicin treatment groups, with the degree of decrease depending on the dose. These results are consistent with the histopathological features, suggesting that our ultrasound imaging-based metric can quantify the anisotropic mechanics of cardiac tissues and potentially be used for the early diagnosis of heart disease.

Evaluation of Machine Learning Techniques Towards Early Detection of Cardiovascular Diseases

The effectiveness of three Machine Learning (ML) algorithms: Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN) techniques for the early diagnosis of heart diseases were evaluated. Heart disease’ dataset collected from kaggle.com data repository, which comprised of 303 data points with 13 features and a target variable were used and data preprocessing by data shuffling and dimension reduction were performed. The new dimension of the dataset was chosen such that 85.03% of the original information is retained. The preprocessed dataset was partitioned into 70% of the training set and 30% of the testing set. The ML algorithms were trained and tested for the diagnosis of cardiovascular diseases (CVD). The training performances of these models were evaluated with a k-fold cross-validation algorithm using 10 folds. The k-fold accuracy shows KNN with an accuracy of 0.837662, RF with an accuracy of 0.834091, and SVM with an accuracy of 0.814935. The test results also show KNN with an accuracy of 0.8, SVM with an accuracy of 0.7889, and RF with an accuracy of 0.7667. KNN emerged the best model both in training and test’s performances and is recommended for the early diagnosis of CVD.

Activatable Conformal Electrode Arrays for Highly Stable and Breathable 12‐Lead Clinical Electrocardiographic Monitoring

AbstractIn clinical practices, ambulatory electrocardiography (ECG) signals are a significant index in early diagnosis of heart diseases. Commercial 12‐lead Holter monitors are widely used to evaluate the heartbeat over time. However, poor sweat‐proof adhesion, low water vapor transmittance, and wired connectivity of Holter monitors limit their popularization in wearable electronics. Herein, a facile, low‐cost, and scalable stretchable supramolecular material with activatable high adhesion is reported, which can be used to fabricate breathable on‐skin electrode arrays for stable 12‐lead ECG monitoring applications. In the proposed strategy, a supramolecular polymer network with various dynamic chemical bonds is obtained, resulting in the material exhibiting good stretchability. The introduction of tannic acid (TA) ensure high intrinsic adhesion at wet/dry skin surfaces. Moreover, the combination of poly (lipoic acid) and TA can realize alcohol‐activated high adhesion. Subsequently, electrode arrays are assembled using adhesive layers comprising supramolecular materials and a stretchable conductive Styrene Ethylene Butylene Styrene (SEBS)/Au layer. In addition, the micromesh structures obtained by laser scribing endowed the electrodes with good water/air permeability. Finally, the breathable on‐skin electrode arrays are combined with flexible ECG sensors for stable 12‐lead ECG monitoring.

Heart Disease Prediction Model

Abstract: Heart disease is a leading cause of death worldwide. Early prediction of heart disease can save many lives. Data mining techniques have been widely used to predict heart disease. In this paper, we present a comprehensive study on heart disease prediction using data mining techniques. We analyse the various data mining algorithms that have been used in the literature for heart disease prediction. We also evaluate the performance of these algorithms using several metrics such as accuracy, precision, recall, and F1 score. We conclude that data mining techniques are useful in heart disease prediction and can help in early diagnosis of heart disease.

Prediction of Heart Disease Using a Hybrid XGBoost-GA Algorithm with Principal Component Analysis: A Real Case Study

Cardiovascular diseases are one of the most common causes of death in the world. At this point, early diagnosis of heart diseases is critically important. The aim of this study is to predict the heart disease using feature selection, classification and optimization algorithms. Firstly, principal component analysis (PCA) is used to create the feature selection model and to determine the effective attributes. Then, Extreme Gradient Boosting (XGBoost) classification model is proposed to predict the heart disease. Finally, genetic algorithm (GA) is applied to optimize the parameters of XGBoost to improve the classification accuracy. The developed hybrid PCA-XGBoost-GA approach is compared with XGBoost, PCA-XGBoost, XGBoost-GA, artificial neural network (ANN) and support vector machine (SVM). The effectiveness of these approaches is illustrated with a case study with the actual data taken from a university hospital in Turkey. The numerical results show that the proposed PCA-XGBoost-GA model outperforms the other classification models in terms of accuracy rate, recall, precision and F-measure. Moreover, feature selection and parameter optimization improve the classification performance of the XGBoost model.

Phonocardiogram signal classification for the detection of heart valve diseases using robust conglomerated models

The diagnosis of cardiovascular diseases is quite important in the field of medical community. An important physiological signal of human body is heart sound and it arises due to the blood turbulence and pulsing of cardiac structures. For the early diagnosis of heart diseases, the analysis of heart sounds play an important role as they contain a huge quantity of pathological information associated with heart. To detect heart sounds, Phonocardiogram (PCG) is used as it is a highly useful and non-invasive technique and can be easily analyzed well. In this paper, some efficient models are proposed for the classification of PCG signals. Two important and robust conglomerated models are proposed initially, wherein the first strategy utilizes the concept of semi-supervised Non-negative Matrix Factorization (NMF) along with Brain Storming (BS) optimization algorithm and an advanced version of BS termed as Advanced BS (ABS) is proposed and then it is merged with Genetic Programming (GP) so that new algorithms such as BS-GP and ABS-GP are formed and finally the features selected through it are fed to classification through machine learning. The second strategy utilizes the concept of using three dimensionality reduction techniques along with Fuzzy C-means (FCM) clustering and then an Advanced Sine-Cosine (ASC) optimization algorithm with three different modifications is proposed for the purpose of feature selection and finally it is classified. Deep learning techniques were also employed in the study such as the usage of an Attention based Bidirectional Long Short-Term Memory (A-BLSTM), Ordinal Variational Autoencoder (O-VAE), Conditional Variational Autoencoders (CVAE), Hyperspherical CVAE (H-CVAE) and the Restricted Boltzmann Machine based Deep Belief Network (RBM-DBN) for the classification of PCG signals. The experiment is conducted on a publicly available dataset and results show that a high classification accuracy of 95.39% is obtained for the semi-supervised NMF concept with ABS-GP technique and Support Vector Machine (SVM) classifier.

심전도 기반의 심장 질환 분류를 위한 신호 특징 추출 시스템 개발

In this paper, we propose a signal-based feature detection system for the early diagnosis of heart disease. The purpose of this study is to develop a compact healthcare system that extracts features from ECG signals. Therefore, the performance of the Tompkins algorithm, which is widely known as a signal-based feature detection algorithm, and the deep learning model of prior research are compared. In addition, by verifying the performance of the deep learning model by applying the Haar wavelet transform to the preprocessing, we determine the optimal feature detection model applicable to the healthcare system in terms of speed and accuracy. All algorithms and models were developed using Matlab on Window, and performance was compared on Jetson nano and Raspberry pi embedded boards. As a result, the best model in terms of speed and accuracy is the Haar wavelet bidirectional long short-term memory model, which quickly performs classification prediction with almost 97% accuracy. The advantage of this study is that it quickly performs classification prediction with high accuracy compared to existing healthcare systems that use algorithms that cause signal distortion. Therefore, cardiovascular diseases can be predicted and monitored based on the feature regions detected in this study. The data used are the MIT-BIH Arrhythmia Database and the Normal Sinus Rhythm Database. If additional patient data are established and verified in the future, it will be possible to use them not only in real life but also in clinical settings.

Diagnosing heart disease by a novel hybrid method: Effective learning approach

In this paper, the effective classifier Extreme Learning Machines (ELM), Enhanced Fast Learning Networks (EFLN), Support Vector Machines (SVM), and Decision Trees (DT) for early Heart Disease (HD) diagnostics have been investigated. Using the optimal parameters for the proposed method, ELM, EFLN, SVM, and DT are considered for early diagnosis of heart disease. The performance of the proposed method has been evaluated versus the Accuracy (ACC), Sensitivity (SE), and Specificity (SP) HD dataset. This study also proposed a new effective Hybrid Model, by combining the Particle Swarm Optimization Algorithm (PSOA) with the most effective Learning classifiers. The proposed hybrid diagnostic method records ACC through ten runs for a 10-fold cross-validation (CV). According to the results, the proposed method shows an ACC of 93% for (PSO-ELM), 96% (PSO_EFLN), 91% (PSO-SVM) and 93% (PSO-DT).

A Review on Machine Learning-Based Algorithms for Heart Disease Diagnosis and Prediction

This work concentrates on different machine learning techniques that can be applied for the early diagnosis of heart diseases. Different Machine Learning Algorithms like KNN (K-Nearest Neighbour), Decision tree, Logistic Regression, Support Vector Machine, Random Forest and Naïve Bayes are used for prediction and detecting heart disease. The main purpose of this survey is to pay our attention towards machine learning techniques for diagnosing heart diseases in an efficient and effective manner.

AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring

Cardiac disorders are one of the leading causes of mortality around the globe and early diagnosis of heart diseases can be beneficial for its mitigation. In this article, an artificial intelligence (AI) based device has been proposed, which allows for an automatic and real-time diagnosis of cardiac diseases based on deep learning techniques. The heart sound (phonocardiogram) signal is acquired by a customized designed stethoscope and the signal is processed before analysis using AI methods for the classification of four major cardiac diseases (Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, and Mitral Valve Prolapse). Two deep learning-based neural networks, one-dimensional (1-D) convolutional neural network (CNN) and spectrogram based 2-D-CNN models from the analysis of these signals has been integrated with a low-cost single-board processor to make a standalone device. All data processing is done in a single hardware setup and user interface is provided allowing the user to control the data accessibility and visibility to generate the diagnostic report. As a result, the developed device has demonstrated to be a valuable low-cost diagnostic tool for both medical professionals and personal usage at home.

Cardiac anomaly detection considering an additive noise and convolutional distortion model of heart sound recordings

Cardiac auscultation is an essential point-of-care method used for the early diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent degradation. This paper aims to develop methods to address the cardiac abnormality detection problem when both of these components are present in the cardiac auscultation sound. We first mathematically analyze the effect of additive noise and convolutional distortion on short-term mel-filterbank energy-based features and a Convolutional Neural Network (CNN) layer. Based on the analysis, we propose a combination of linear and logarithmic spectrogram-image features. These 2D features are provided as input to a residual CNN network (ResNet) for heart sound abnormality detection. Experimental validation is performed first on an open-access, multiclass heart sound dataset where we analyzed the effect of additive noise by mixing lung sound noise with the recordings. In noisy conditions, the proposed method outperforms one of the best-performing methods in the literature achieving an Macc (mean of sensitivity and specificity) of 89.55% and an average F-1 score of 82.96%, respectively, when averaged over all noise levels. Next, we perform heart sound abnormality detection (binary classification) experiments on the 2016 Physionet/CinC Challenge dataset that involves noisy recordings obtained from multiple stethoscope sensors. The proposed method achieves significantly improved results compared to the conventional approaches on this dataset, in the presence of both additive noise and channel distortion, with an area under the ROC (receiver operating characteristics) curve (AUC) of 91.36%, F-1 score of 84.09%, and Macc of 85.08%. We also show that the proposed method shows the best mean accuracy across different source domains, including stethoscope and noise variability, demonstrating its effectiveness in different recording conditions. The proposed combination of linear and logarithmic features along with the ResNet classifier effectively minimizes the impact of background noise and sensor variability for classifying phonocardiogram (PCG) signals. The method thus paves the way toward developing computer-aided cardiac auscultation systems in noisy environments using low-cost stethoscopes.

A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment.

With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset of features. Moreover, an attention-based convolutional neural network (ACNN) with long short-term memory (LSTM), called (ACNN-LSTM) model, is utilized for the detection of HD by using medical data. An extensive experimental study is performed to ensure the improved classification performance of the FSHDL-HDDC technique. A detailed comparison study reported the betterment of the FSHDL-HDDC method on existing techniques interms of different performance measures. The suggested system, the FSHDL-HDDC, has reached its maximum level of accuracy, which is 0.9772.