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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Ramakrishna, Jeevakala Siva
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Development of explainable machine intelligence models for heart sound abnormality detection Ramakrishna, Jeevakala Siva; Venkateswarlu, Sonagiri China; Kumar, Kommu Naveen; Shreya, Parikipandla
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp846-853

Abstract

Developing explainable machine intelligence (XAI) models for heart sound abnormality detection is a crucial area of research aimed at improving the interpretability and transparency of machine learning algorithms in medical diagnostics. In this study, we propose a framework for building XAI models that can effectively detect abnormalities in heart sounds while providing interpretable explanations for their predictions. We leverage techniques such as SHapley additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) to generate explanations for model predictions, enabling clinicians to understand the rationale behind the algorithm’s decisions. Our approach involves preprocessing heart sound data, training machine learning models, and integrating XAI techniques to enhance the interpretability of the models. We evaluate the performance of our XAI models using standard metrics and demonstrate their effectiveness in accurately detecting heart sound abnormalities while providing insightful explanations for their predictions. This research contributes to the advancement of XAI in medical applications, particularly in the domain of cardiac diagnostics, where interpretability is crucial for clinical decision-making.
Co-Authors Kumar, Kommu Naveen Shreya, Parikipandla Venkateswarlu, Sonagiri China