Article

An Explainable Transfer Learning based Residual Attention BiLSTM Model for Fair and Accurate Prognosis of Ischemic Heart Disease

2025-07-04

Abstract excerpt

<h4>Background: </h4> Early and accurate prediction of Ischemic Heart Disease (IHD) is critical to reducing cardiovascular mortality through timely intervention. While deep learning (DL) models have shown promise in disease prediction, many lack interpretability, generalizability, and fairness—particularly when deployed across demographically diverse populations. These shortcomings limit clinical adoption and risk...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: b1300be3-8796-5a51-b43a-2011f1a03178
DOI: 10.12688/f1000research.166307.1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

An Explainable Transfer Learning based Residual Attention BiLSTM Model for Fair and Accurate Prognosis of Ischemic Heart DiseaseDOI 10.12688/f1000research.166307.1Read

Explainable Transfer Learning with Residual Attention BiLSTM for Prognosis of Ischemic Heart Disease.Semantic proximity Read

An Explainable Self-Supervised Learning Framework for Interpretable and Accurate Heart Disease Prediction Using EDA–SimCLR–SHAP PipelineSemantic proximity Read

Ensemble Post-hoc Explainable AI for Multilead ECG: Identifying Disease-Relevant Features in Single-Lead InterpretationsSemantic proximity Read

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV DatasetSemantic proximity Read

Explainable Artificial Intelligence for the Detection and Characterisation of Stage B Heart FailureSemantic proximity Read

Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational StudySemantic proximity Read

A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at ScaleSemantic proximity Read

A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary functionSemantic proximity Read

Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical CareSemantic proximity Read

CardioAI: An Explainable Machine Learning System for Cardiovascular Risk Prediction and Patient Retention in Nigerian Healthcare SettingsSemantic proximity Read

A Deep Learning Approach for Prediction of Heart Disease using Ensemble Learning TechniquesSemantic proximity Read

Heart Disease Prediction Redefined: Hybrid CNN & MobileNet Model ComparisonSemantic proximity Read