AIMC Topic: Heart Diseases
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Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation.
BMC cardiovascular disorders
Jun 2, 2025
OBJECTIVE: Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (AF). This study aimed to evaluate the incidence and predictors of LAT in patients with paroxysmal AF...
Optimized machine learning mechanism for big data healthcare system to predict disease risk factor.
Scientific reports
Apr 24, 2025
Heart disease is becoming more and more common in modern society because of factors like stress, inadequate diets, etc. Early identification of heart disease risk factors is essential as it allows for treatment plans that may reduce the risk of sever...
Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets.
Scientific reports
Apr 22, 2025
Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagn...
Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy.
Scientific reports
Apr 18, 2025
This primary research paper emphasizes cross-validation, where data samples are reshuffled in each iteration to form randomized subsets divided into n folds. This method improves model performance and achieves higher accuracy than the baseline model....
A hybrid approach with metaheuristic optimization and random forest in improving heart disease prediction.
Scientific reports
Mar 31, 2025
Cardiovascular diseases (CVD) a major cause of morbidity and mortality among the world's non-communicable disease incidences. Though these practices are in use for diagnostics of different CVDs in clinical settings, need improvement because they are...
Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated cardiomyopathy: a retrospective study.
BMC cardiovascular disorders
Mar 27, 2025
BACKGROUND: Systemic embolic events due to exfoliation of intracardiac thrombus (ICT) are one of the catastrophic complications of dilated cardiomyopathy (DCM). This study intended to develop a prediction model to predict the risk of ICT in patients ...
Optimizing heart disease diagnosis with advanced machine learning models: a comparison of predictive performance.
BMC cardiovascular disorders
Mar 22, 2025
Cardiovascular disease is the leading cause of mortality globally, necessitating precise and prompt predictive instruments to enhance patient outcomes. In recent years, machine learning methodologies have demonstrated significant potential in enhanci...
Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.
Scientific reports
Mar 8, 2025
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
Feb 20, 2025
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
Feb 19, 2025
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...
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