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Heart Diseases

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Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection.

Computational intelligence and neuroscience
Heart disease causes major death across the entire globe. Hence, heart disease prediction is a vital part of medical data analysis. Recently, various data mining and machine learning practices have been utilized to detect heart disease. However, thes...

Finding the influential clinical traits that impact on the diagnosis of heart disease using statistical and machine-learning techniques.

Scientific reports
In recent years, the omnipresence of cardiac problems has been recognized as an epidemic. With the correct and quick diagnosis, both mortality and morbidity from cardiac disorders can be dramatically reduced. However, frequent medical check-ups are p...

Heart disease prediction using IoT based framework and improved deep learning approach: Medical application.

Medical engineering & physics
Heart disease is the biggest cause of death in the globe. The method of predicting cardiac disease is exceedingly complex. It can only be done properly if the doctor has a lot of expertise and is well-versed in the condition. IoT-based illness predic...

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network.

Sensors (Basel, Switzerland)
The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret ...

Construction of a new smooth support vector machine model and its application in heart disease diagnosis.

PloS one
Support vector machine (SVM) is a new machine learning method developed from statistical learning theory. Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of fast optimization algorithms can't be used to fin...

An Overview of Deep Learning Methods for Left Ventricle Segmentation.

Computational intelligence and neuroscience
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning arc...

Deep learning-based diagnosis of feline hypertrophic cardiomyopathy.

PloS one
Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10-15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods ...

Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis.

Scientific reports
Right ventricular (RV) volumes are commonly obtained through time-consuming manual delineations of cardiac magnetic resonance (CMR) images. Deep learning-based methods can generate RV delineations, but few studies have assessed their ability to accel...

A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.

Scientific reports
Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements in the analysis of echocardiography, the major limitations remain to be the built neural networks are...

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals.

Computers in biology and medicine
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess t...