Classification Of Heart Disease Research Articles

Hybrid Feature Selection with Chaotic Rat Swarm Optimization-Based Convolutional Neural Network for Heart Disease Prediction from Imbalanced Datasets

Introduction: Early diagnosis of Cardiovascular Disease (CVD) is vital in reducing mortality rates. Artificial intelligence and machine learning algorithms have increased the CVD prediction capability of clinical decision support systems. However, the shallow feature learning in machine learning and incompetent feature selection methods still pose a greater challenge. Consequently, deep learning algorithms are needed to improvise the CVD prediction frameworks. Methods: This paper proposes an advanced CDSS for CVD detection using a hybrid DL method. Initially, the Improved Hierarchical Density-based Spatial Clustering of Applications with Noise (IHDBSCAN), Adaptive Class Median-based Missing Value Imputation (ACMMVI) and Clustering Using Representatives-Adaptive Synthetic Sampling (CURE-ADASYN) approaches are introduced in the pre-processing stage for enhancing the input quality by solving the problems of outliers, missing values and class imbalance, respectively. Then, the features are extracted, and optimal feature subsets are selected using the hybrid model of Information gain with Improved Owl Optimization algorithm (IG-IOOA), where OOA is improved by enhancing the search functions of the local search process. These selected features are fed to the proposed Chaotic Rat Swarm Optimization-based Convolutional Neural Networks (CRSO-CNN) classifier model for detecting heart disease. Results: Four UCI datasets are used to validate the proposed framework, and the results showed that the OOA-DLSO-ELM-based approach provides better heart disease prediction with high accuracy of 97.57%, 97.32%, 96.254% and 97.37% for the four datasets. Conclusions: Therefore, this proposed CRSO-CNN model improves the heart disease classification with reduced time complexity for all four UCI datasets.

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease. The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)-based SpinalNet. The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO). The JSFRO-based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%.

The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG)

Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recorded from multiple locations on the chest of subjects and some conventional time domain indices are widely used in Machine learning (ML) classifiers to objectively distinguish IHD and control subjects. Most of the earlier studies have employed features that are derived from signal-averaged cardiac beats and have ignored inter-beat information. The present study demonstrates the utility of beat-by-beat features to be useful in classifying IHD subjects (n = 23) and healthy controls (n = 75) in 37-channel MCG data taken under rest condition of subjects. The study reveals the importance of three features (out of eight measured features) namely, the field map angle (FMA) computed from magnetic field map, beat-by-beat variations of alpha angle in the ST-T region and T wave magnitude variations in yielding a better classification accuracy (92.7 %) against that achieved by conventional features (81 %). Further, beat-by-beat features are also found to augment the accuracy in classifying myocardial infarction (MI) Versus control subjects in two public ECG databases (92 % from 88 % and 94 % from 77 %). These demonstrations summarily suggest the importance of beat-by-beat features in clinical diagnosis of ischemia.

CB-HDM: ECG signal based heart disease classification using convolutional block attention assisted hybrid deep Maxout network

Cardiovascular diseases (CVD) stand as the leading cause of death globally. Diagnosis of CVD relies on electrocardiograms (ECGs), which record the heart’s activity. Adequate labeled data is essential for effectively training machine learning techniques, which may also demand considerable time and memory resources. To address these existing issues, this research introduces an improved detection technique for predicting CVD. Initially, data is sourced from the MIT-BIH arrhythmia database, followed by pre-processing using the Modified FIR filter model (M-FIR) to filter ECG signals. It is used to enhance signal quality and eliminate unwanted noise. M-FIR is used to preserve the specific features of the signal. Feature extraction utilizes the Radial Hilbert Function Transform Network (RHFTN) technique. It can extract the temporal and spectral signal features for effective CVD detection. The Improved Bald Eagle Search Algorithm (IBES) is employed to select features and minimize irrelevant features. Detection and classification of heart disease from ECG signals are achieved using the Convolutional Block Attention Assisted Hybrid Deep Maxout Network model (CB-HDM). Losses in the network model are mitigated by the Gazelle Optimization Algorithm (GOA). The evaluation results are simulated using Python. The proposed model achieved 98.81% accuracy, 96.18% precision, 96.87% recall, and a 94.39% F1 score. The proposed model demonstrates superior efficiency compared to other classifiers.

Enhanced Detection of Chronic Heart Disease: A Novel Approach With Extreme Gradient Boosting-Logistic Regression Algorithm

In recent years, biomedical healthcare applications for managing and treating chronic heart diseases have increased. Chronic heart disease is a serious and sometimes deadly ailment that affects millions of individuals globally. Therefore, timely and effective detection of heart disease is crucial in medicine, particularly in cardiology. These complications can lead to reduced quality of life, increased medical costs, and increased risk of death. Obesity, smoking, and high blood pressure are some of the risk factors that worsen chronic heart disease. However, challenges occur in accurately and sensitively classifying heart disease in performance evaluation. To resolve this problem, we propose an Extreme Gradient Boosting-Logistic Regression (EGBLR) algorithm to accurately and quickly predict chronic heart disease. Furthermore, pre-processing can be performed using the Normalized Minimum-Maximum Scaling Feature (NMMSF) technique to estimate missing or duplicate values. Next, the Inter-Quartile Range Optimization (IQRO) technique can analyze heart disease data to identify and remove outliers. After that, the Relief Feature Vector (RFV) algorithm can be used to find each feature weight. Finally, the proposed EGBLR method can be employed to detect normal or abnormal chronic heart diseases and increase accuracy in diagnosis. The evaluation of the proposed techniques can be predicted using performance matrices such as recall, sensitivity score, precision, false rate, and precision. The accuracy of the proposed EGBLR detection approach exceeds that of previous techniques. Moreover, the proposed method for diagnosing chronic heart disease can be easily adapted to biomedical healthcare applications.

Cloud computing-based framework for heart disease classification using quantum machine learning approach

Abstract Accurate early identification and treatment of cardiovascular diseases can prevent heart failure problems and reduce mortality rates. This study aims to use quantum learning to predict heart problems to increase the accuracy of traditional prediction and classification methods. Machine learning (ML) and deep learning (DL) techniques need quantum learning to quickly and accurately analyze massive volumes of complex data. With quantum computing, the suggested DL and ML algorithms can change their predictions on the basis of changes in the dataset. This approach could help with the early and accurate detection of chronic diseases. The Cleveland heart disease dataset is undergoing preliminary processing to validate missing values to increase the precision rate and prevent incorrect forecasts. This study examined the feasibility of employing and deploying a quantum ML (QML) framework via cloud computing to categorize cardiac conditions. The research was divided into four sections. First, the principal component analysis was used to preprocess the Cleveland dataset, recursive feature elimination was used to select features, and min–max normalization was used to give the dataset a high-dimensional value. Second, we compared traditional classifiers, such as support vector machine (SVM) and artificial neural network, with the quantum approach to verify the quantum approach’s efficiency. Third, we examined two unique QML classification methods: quantum neural networks (QNNs) and quantum SVM (QSVM). Fourth, bagging-QSVM was developed and deployed as an ensemble learning model. Experimental results using the QNN show an accuracy of 77%, a precision of 76%, a recall of 73%, and an F1 score of 75%. With an accuracy of 85%, a precision of 79%, a recall of 90%, and an F1-score of 84%, the QSVM method demonstrated a much better performance than the QNN. Particularly, the Bagging_QSVM model exhibited an outstanding performance, with a flawless score of 100% across all critical performance measures. The study shows that the bagging method for ensemble learning is a solid way of increasing the accuracy of quantum method predictions.

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.

Classification of Heart Disease Using Modified K-Nearest Neighbor (MKNN) Method

Penyakit jantung memiliki banyak jenis dan gejala yang dialami. Penyakit jantung adalah sebuah kondisi ketika organ jantung tidak dapat bekerja sebagaimana fungsinya dengan baik. Jantung adalah organ penting dalam tubuh manusia yang dimana fungsinya adalah memompa darah ke seluruh tubuh. Karena itu dibutuhkannya diagnosa awal untuk pencegahan penyakit jantung dengan memanfaatkan system yang dapat dibuat untuk diagnosa awal pada gejala yang dialami. Yang pada penelitian ini akan menggunakan metode Modified K-Nearest Neighbor (MKNN) dalam mengklasifikasikan penyakit jantung berdasarkan kriteria atau gejala yang ada. Penelitian ini menggunakan 6 kriteria penyakit dan 3 kelas diagnosa penyakit jantung. Dengan melewati beberapa langkah pengerjaan yaitu menghitung jarak Euclidean, menghitung nilai validitas dan terakhir menghitung weight voting dengan mengandalkan nilai K yang telah ditentukan sejak awal perhitungan. Pada penelitian ini telah ditentukan nilai K=5 dan didapat hasil pengujian akurasi sebesar 85%, dengan recall 90% dan precision 85%.

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.

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.

An Extensive Review of Machine Learning and Deep Learning Techniques on Heart Disease Classification and Prediction

An Extensive Review of Machine Learning and Deep Learning Techniques on Heart Disease Classification and Prediction

Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest

Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest

Classification of Heart Diseases with ECG Monitoring Using MATLAB

Classification of Heart Diseases with ECG Monitoring Using MATLAB

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.

Retraction Note: High-performance in classification of heart disease using advanced supercomputing technique with cluster-based enhanced deep genetic algorithm

Retraction Note: High-performance in classification of heart disease using advanced supercomputing technique with cluster-based enhanced deep genetic algorithm

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.

Classification, Diagnosis, and Treatment of Obesity-Related Heart Diseases.

Evidence-based medicine shows that obesity is associated with a wide range of cardiovascular (CV) diseases. Obesity can lead to changes in cardiac structure and function, which can lead to obese cardiomyopathy, subclinical cardiac dysfunction, and even heart failure. It also increases the risk of atrial fibrillation and sudden cardiac death. Many invasive and noninvasive diagnostic methods can detect obesity-related heart disease at an early stage, so that appropriate measures can be selected to prevent adverse CV events. However, studies have shown a protective effect of obesity on clinical outcomes of CV disease, a phenomenon that has been termed the obesity paradox. The "obesity paradox" essentially refers to the fact that the classification of obesity defined by body mass index (BMI) does not consider the impact of obesity heterogeneity on CV disease prognosis, but simply puts subjects with different clinical and biochemical characteristics into the same category. In any case, indicators such as waist-to-hip ratio, ectopic body fat qualitative and quantitative, and CV fitness have been shown to be able to distinguish different CV risks in patients with the same BMI, which is convenient for early intervention in an appropriate way. A multidisciplinary approach, including lifestyle modification, evidence-based generic and novel pharmacotherapy, and surgical intervention, can improve CV outcomes in overweight/obese patients.

XGBoost and Random Forest Optimization using SMOTE to Classify Air Quality

Air pollution due to the growth of industry and motorized vehicles seriously threatens human health. Clean air is essential, but pollutant contamination can cause acute respiratory illnesses and other illnesses. Several studies have been carried out to anticipate this air pollution. Various algorithms, methods, and data balancing techniques have been implemented, but still need to be done to obtain better accuracy results. Therefore, this study aims to classify heart disease using the XGBoost and Random Forest algorithms and implement the SMOTE technique to overcome data imbalance. This research produces a Random Forest algorithm with SMOTE implementation with splitting 80:20, which has the best accuracy with an accuracy of 92.4%, an average AUC of 0.98, and a log loss of 0.2366, which shows that SMOTE has succeeded in improving model performance in classifying minority classes. Based on the results obtained, the XGBoost and Random Forest algorithms after SMOTE are superior to the model with SMOTE, with accuracy above 90%.

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

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

ECG based one-dimensional residual deep convolutional auto-encoder model for heart disease classification

ECG based one-dimensional residual deep convolutional auto-encoder model for heart disease classification