Search Results for author:
Found 2 papers, 0 papers with code
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
It breaks down the interpretability process into data pre-processing, model selection, and post-processing, aiming to foster a comprehensive understanding of the crucial role of a robust interpretability approach in healthcare and to guide future research in this area.
AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach
The accuracy of coronary artery disease (CAD) diagnosis is dependent on a variety of factors, including demographic, symptom, and medical examination, ECG, and echocardiography data, among others.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
