ClusteredSHAP : faster gradient explainer based on k-means clustering and selections of gradients in explaining 12-lead ECG Classification Model

Name: Mo, Bo-Yu

Organization : National Yang-Ming Chiao Tung University. Department of Electronics Engineering

Email : micky199966@gmail.com

Name: Sirapop Nuannimnoi

Organization : National Yang-Ming Chiao Tung University. EECS International Graduate Program

Email : sirapop.ee07@nycu.edu.tw

Name: Baskoro, Angger

Organization : National Yang-Ming Chiao Tung University. EECS International Graduate Program

Email : angger63.ee08@nycu.edu.tw

Name: Khan, Azam

Organization : National Yang-Ming Chiao Tung University. EECS International Graduate Program

Name: Dwi Pratiwi, Jasmine Ariesta

Organization : National Yang-Ming Chiao Tung University. International Health Program

Email : jariesta31@gmail.com

Name: Huang, Ching-Yao

Organization : National Yang-Ming Chiao Tung University. Department of Electronics Engineering

Email : cyhuang@nycu.edu.tw

keyword: Clustering algorithms

LCSH: Electrocardiography

; Explainable artificial intelligence

LCSH: Machine learning

; Medical signal processing

LCSH: Neural networks (Computer science)

Abstract: The vast majority of healthcare systems are operating at or near their full capacity. Providing an inaccurate diagnosis is a further prevalent issue. Although it is common knowledge that physicians get significant training, it is nevertheless possible for them to misdiagnose patients, overlook warning signs, or commit any number of other human blunders. Deep Learning (DL) has emerged as a helpful tool in medical diagnostics, particularly in ECG (electrocardiogram) signal classification, enabling efficient and precise detection of cardiac abnormalities. However, the inherent black-box nature of DL poses challenges for its direct implementation in real-world medical settings. To address this, Explainable AI (XAI) techniques such as SHAP, LIME, and CAM have been introduced, aiming to render the complex decisions of neural networks interpretable. Among these, SHAP is recognized for its robustness and comprehensive explanation capabilities. Nonetheless, the high computational demands of traditional SHAP methods hinder their real-time application, especially in urgent medical scenarios. In this paper, we propose an optimized SHAP approach leveraging K-Means clustering to group gradients by importance, namely ClusteredSHAP. Our methodology focuses on a select cluster of high-magnitude gradients, enhancing the efficiency of the GradientExplainer. Our evaluation encompasses both the computational efficiency and the usability of the resulting explanations, through a custom questionnaire and explanation usability scores inspired by established user experience metrics and medical tool usability standards. The results show that our proposed ClusteredSHAP not only provides

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2023

Modified: 2025-03-04

Issued: 2025-03-04

บทความ/Article

application/pdf

BibliograpyCitation : In King Mongkut's University of Technology Thonburi. The 13th International Conference on Advances in Information Technology (IAIT 2023) (Article 27). Bangkok : King Mongkut's University of Technology Thonburi

eng

©copyrights King Mongkut's University of Technology North Bangkok

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