IEEE

Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection

Abstract: Cardiovascular disease (CVD) is a prevalent and serious condition causing a significant global mortality rate. According to the World Health Organization (WHO), in 2022, CVD claimed the ...
GitHub

ECGTwinMentor simulates a digital twin of an ECG diagnostic system. Users can input or ...

Version 1.0.0 consolidates the architecture, machine learning model, and multiplatform deployment strategy for educational and predictive use of ECG data. ECGTwinMentor simulates a digital twin of an ...
Frontiers

Brain-Computer Interfaces (BCIs) for daily activities: Innovations in EEG signal analysis ...

Brain-Computer Interfaces (BCIs) are emerging as transformative tools that enable direct communication between the human brain and external devices. With recent advancements in Electroencephalography ...
IEEE

Detecting Arrhythmias Using ECG Signals with Machine Learning Techniques

Abstract: This paper presents a comparative study on the effectiveness of various feature selection methods and machine learning algorithms for arrhythmia prediction using electrocardiogram (ECG) ...
Scientific Research Publishing

Leveraging Pima Dataset to Diabetes Prediction: Case Study of Deep Neural Network ()

Laboratory of Data Science and Digital Inclusion, Institut de Mathématiques et de Sciences Physiques, Université d’Abomey-Calavi, Abomey-Calavi, Bénin. Diabetes is a chronic disease. In 2019, it was ...
ahajournals.org

Multinational Federated Learning Approach to Train ECG and Echocardiogram Models for ...

Novel targeted treatments increase the need for prompt hypertrophic cardiomyopathy (HCM) detection. However, its low prevalence (0.5%) and resemblance to common diseases present challenges that may ...