AIMC Topic: Heart Diseases

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Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models.

Computational and mathematical methods in medicine
Accurate prediction of cardiovascular disease is necessary and considered to be a difficult attempt to treat a patient effectively before a heart attack occurs. According to recent studies, heart disease is said to be one of the leading origins of de...

Heuristic-based channel selection with enhanced deep learning for heart disease prediction under WBAN.

Computer methods in biomechanics and biomedical engineering
The main intention of this proposal is to design and develop a new heart disease prediction model via WBAN using three stages. The first stage is data aggregation, in which data is scheduled in Time Division Multiple Access manner based on priority l...

Clinical applicability of artificial intelligence for patients with an inherited heart disease: A scoping review.

Trends in cardiovascular medicine
The number of inherited heart disease (IHD) studies using artificial intelligence (AI) has increased rapidly over the last years. In this scoping review, we aimed to present an overview of the current literature available on the applicability of AI w...

Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network.

Computational and mathematical methods in medicine
Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG d...

Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model.

Computational intelligence and neuroscience
Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wea...

Use of machine learning to classify high-risk variants of uncertain significance in lamin A/C cardiac disease.

Heart rhythm
BACKGROUND: Variation in lamin A/C results in a spectrum of clinical disease, including arrhythmias and cardiomyopathy. Benign variation is rare, and classification of LMNA missense variants via in silico prediction tools results in a high rate of va...

Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine.

Computational intelligence and neuroscience
Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting...

Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy.

Cardiovascular toxicology
Cardiotoxicity is a severe side effect for colorectal cancer (CRC) patients undergoing fluoropyrimidine-based chemotherapy. To develop and compare machine learning algorithms to predict cardiotoxicity risk among nationally representative CRC patients...

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer.

Computational intelligence and neuroscience
Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for p...

Robust optimization of convolutional neural networks with a uniform experiment design method: a case of phonocardiogram testing in patients with heart diseases.

BMC bioinformatics
BACKGROUND: Heart sound measurement is crucial for analyzing and diagnosing patients with heart diseases. This study employed phonocardiogram signals as the input signal for heart disease analysis due to the accessibility of the respective method. Th...