Computational Intelligence and Machine Learning
Impact of Interpretability on Bias in ECG-Based Models for Cardiac Disease Detection
Authors: Saroj Kumari and Raghav Mehra
Publishing Date: 30-04-2025
ISBN: 978-81-975670-5-6
Abstract
Cardiac disease is a major health issue worldwide and a leading cause of mortality. Recent advancements in machine learning (ML) show potential for early heart disease detection using patient data and electrocardiograms (ECGs). Early identification can significantly reduce death rates and mitigate the impact of heart disease. Delayed and misdiagnosis therapeutic are two major issues with the traditional diagnostic approaches that can worsen diseases and shoot up the healthcare expenses. To cut these medical costs and avoid any faulty diagnostic ML techniques provides a promising solution in health care sector. The most noninvasive and affordable technique adopted for heart diagnostic by healthcare professionals is ECG. In medical ECG is widely used for the diagnosis, detection, and prevention of many cardiac problems. Disregard of many advantages, there are still issues, such as the lack of qualified cardiologists, comorbidities, and the resemblance of heart disease symptoms in ECG readings. Additionally, patient data and ECGs are often unbalanced, complicating the impartial performance of classical ML models. Traditional ECG diagnostics has improved by applying ML techniques and, helping doctors in interpreting complex cardiac disease processes and boosting computer-assisted treatments. Despite their potential, ML models face skepticism from medical professionals due to their “black box” nature and poor explainability. Many ML models for ECG-based heart diseases detection suffer from bias and lack transparency, raising ethical, legal, and social concerns. To address this, interpretable machine learning (IML) models can boost doctor confidence by providing evidence-based, understandable diagnoses. The detection of cardiac diseases using ECG data, investigating bias and fairness in IML models, and suggesting strategies to guarantee equitable model performance across heterogeneous patient populations are the main focus of this systematic literature review.
Keywords
Heart diseases, CVD, ECG, Machine learning, Bias, Interpretability, Explainability.
Cite as
Saroj Kumari and Raghav Mehra, "Impact of Interpretability on Bias in ECG-Based Models for Cardiac Disease Detection", In: Sandeep Kumar and Kavita Sharma (eds), Computational Intelligence and Machine Learning, SCRS, India, 2025, pp. 131-148. https://doi.org/10.56155/978-81-975670-5-6-11