AIMC Topic: Heart Diseases

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A survey on open challenges in heart disease prediction models.

Computational biology and chemistry
Heart disease (HD), is a deadly serious disease, that has received a great deal of consideration in medical study. A lot of prompting factors like professional, as well as personal behaviors and genetic nature, are accountable for the occurrence of H...

Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.

Scientific reports
Detection and classification of cardiovascular diseases are crucial for early diagnosis and prediction of heart-related conditions. Existing methods rely on either electrocardiogram or phonocardiogram signals, resulting in higher false positive rates...

An extensive experimental analysis for heart disease prediction using artificial intelligence techniques.

Scientific reports
The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality globally. Early and accurate detection can significantly improve the situation by enabling preventive ...

An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm.

Scientific reports
Diet, stress, genetics, and a sedentary lifestyle may all contribute to heart disease rates. Although recent studies propose comprehensive automated diagnostic systems, these systems tend to focus on one aspect, such as feature selection, prioritizat...

Neural-symbolic hybrid model for myosin complex in cardiac ventriculum decodes structural bases for inheritable heart disease from its genetic encoding.

Archives of biochemistry and biophysics
BACKGROUND: Human ventriculum myosin (βmys) powers contraction sometimes in complex with myosin binding protein C (MYBPC3). The latter regulates βmys activity and impacts cardiac function. Single residue variants (SRVs) change protein sequence in βmy...

A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning.

BMC geriatrics
OBJECTIVE: Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.

Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data.

Scientific reports
In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often lea...

A novel fuzzy three-valued logic computational framework in machine learning for medicine dataset.

Computers in biology and medicine
For consideration of uncertainties of a medicine dataset, a new conceptual architecture fuzzy three-valued logic is introduced in this research work. The proposed concept is applied to the heart disease dataset for the assessment of heart disease ris...

A smart CardioSenseNet framework with advanced data processing models for precise heart disease detection.

Computers in biology and medicine
Heart diseases remain one of the leading causes of death worldwide. As a result, early and accurate diagnostics have become an urgent need for treatment and management. Most of the conventional methods adopted tend to have major drawbacks: the issues...

Applications and potential of machine, learning augmented chest X-ray interpretation in cardiology.

Minerva cardiology and angiology
The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have rema...