AIMC Topic: Heart Diseases

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Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN).

Scientific reports
Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to pre...

A novel approach for heart disease prediction using hybridized AITHO algorithm and SANFIS classifier.

Network (Bristol, England)
In today's world, heart disease threatens human life owing to higher mortality and morbidity across the globe. The earlier prediction of heart disease engenders interoperability for the treatment of patients and offers better diagnostic recommendatio...

Effective cardiac disease classification using FS-XGB and GWO approach.

Medical engineering & physics
Globally, cardiovascular diseases (CVDs) are a leading cause of death; however, their impact can be greatly mitigated by early detection and treatment. Machine learning (ML)-based algorithms that use features extracted from electrocardiogram (ECG) si...

Accelerated chemical shift encoded cardiovascular magnetic resonance imaging with use of a resolution enhancement network.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (fast chemical shift encoding [FastCSE]) to accelerate CSE.

Refining heart disease prediction accuracy using hybrid machine learning techniques with novel metaheuristic algorithms.

International journal of cardiology
Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart dis...

Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study.

Journal of medical Internet research
BACKGROUND: The presence of bias in artificial intelligence has garnered increased attention, with inequities in algorithmic performance being exposed across the fields of criminal justice, education, and welfare services. In health care, the inequit...

A Shape-Consistent Deep-Learning Segmentation Architecture for Low-Quality and High-Interference Myocardial Contrast Echocardiography.

Ultrasound in medicine & biology
OBJECTIVE: Myocardial contrast echocardiography (MCE) plays a crucial role in diagnosing ischemia, infarction, masses and other cardiac conditions. In the realm of MCE image analysis, accurate and consistent myocardial segmentation results are essent...

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.

Cardiovascular diabetology
BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical prac...

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

Scientific data
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality...