AIMC Topic: Heart Diseases

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Effective prediction of heart disease using hybrid ensemble deep learning and tunicate swarm algorithm.

Journal of biomolecular structure & dynamics
Heart disease (HD) is the major reason for the rampant cause of death around the world. It is deemed as a crucial illness among the middle and old age people which tends to high mortality rates. Recently, Effects of HD is presenting a shocking rise i...

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network.

Journal of healthcare engineering
In recent decades, heart disease threatens people's health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an ea...

Machine Learning for Real-Time Heart Disease Prediction.

IEEE journal of biomedical and health informatics
Heart-related anomalies are among the most common causes of death worldwide. Patients are often asymptomatic until a fatal event happens, and even when they are under observation, trained personnel is needed in order to identify a heart anomaly. In t...

Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison.

Computers in biology and medicine
Machine learning and data mining-based approaches to prediction and detection of heart disease would be of great clinical utility, but are highly challenging to develop. In most countries there is a lack of cardiovascular expertise and a significant ...

Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning.

Computational intelligence and neuroscience
The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis o...

Weighing features of lung and heart regions for thoracic disease classification.

BMC medical imaging
BACKGROUND: Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and he...

Explaining deep neural networks for knowledge discovery in electrocardiogram analysis.

Scientific reports
Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction...

Applications of artificial intelligence and machine learning approaches in echocardiography.

Echocardiography (Mount Kisco, N.Y.)
Artificial intelligence and machine learning approaches have become increasingly applied in the field of echocardiography to streamline diagnostic and prognostic assessments, and to support treatment decisions. Artificial intelligence and machine lea...

Efficient Automated Disease Diagnosis Using Machine Learning Models.

Journal of healthcare engineering
Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagno...

Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global an...