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Heart Diseases

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

Computational intelligence and neuroscience
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 d...

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Computational intelligence and neuroscience
Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressu...

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning.

Computational and mathematical methods in medicine
Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is ...

Machine learning-based heart disease diagnosis: A systematic literature review.

Artificial intelligence in medicine
Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patients' data, de...

rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography.

Circulation
BACKGROUND: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We de...

Deep Time Growing Neural Network vs Convolutional Neural Network for Intelligent Phonocardiography.

Studies in health technology and informatics
This paper explores the capabilities of a sophisticated deep learning method, named Deep Time Growing Neural Network (DTGNN), and compares its possibilities against a generally well-known method, Convolutional Neural network (CNN). The comparison is ...

Effective heart disease prediction with Grey-wolf with Firefly algorithm-differential evolution (GF-DE) for feature selection and weighted ANN classification.

Computer methods in biomechanics and biomedical engineering
In recent time, heart disease has become common leading to mortality of many individuals. Hence, early and accurate prediction of this disease is vital to reduce death rate and enhance people's lives. Concurrently, Artificial Intelligence has gained ...

Compact pediatric cardiac magnetic resonance imaging protocols.

Pediatric radiology
Cardiac MRI is in many respects an ideal modality for pediatric cardiovascular imaging, enabling a complete noninvasive assessment of anatomy, morphology, function and flow in one radiation-free and potentially non-contrast exam. Nonetheless, traditi...

[Artificial intelligence and radiomics : Value in cardiac MRI].

Radiologie (Heidelberg, Germany)
CLINICAL/METHODICAL ISSUE: Cardiac diseases are the leading cause of death. Many diseases can be specifically treated once a valid diagnosis is established. Cardiac magnetic resonance imaging (MRI) plays a central role in the workup of many cardiac p...

Estimation of Cardiac Short Axis Slice Levels with a Cascaded Deep Convolutional and Recurrent Neural Network Model.

Tomography (Ann Arbor, Mich.)
Automatic identification of short axis slice levels in cardiac magnetic resonance imaging (MRI) is important in efficient and precise diagnosis of cardiac disease based on the geometry of the left ventricle. We developed a combined model of convoluti...