Interpretability Evaluation of Rule-Based Classifier in Myocardial Infarction Classification Based on Syntactical Features of ECG Signal

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DOI: https://doi.org/10.21924/cst.10.2.2025.1851
Keywords:
Electrocardiography, Intrinsic Interpretability, Myocardial Infarction, Rule-Based Classifier

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Farhatul Fityah
Master Program of Biomedical Engineering, Universitas Gadjah Mada, Yogyakarta 55284, Indonesia
Noor Akhmad Setiawan
Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Dyah Wulan Anggrahini
Department of Cardiology and Vascular Medicine, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia

Abstract

Cardiovascular diseases remain the leading cause of mortality on a global scale, with myocardial infarction (MI) representing a critical and life-threatening condition. Electrocardiography (ECG) is a widely utilized method for the detection of myocardial infarction (MI), and artificial intelligence (AI) has demonstrated a promising performance in the automated ECG-based diagnosis. However, most existing studies emphasizepredictive accuracy while failing to provide substantial evidence that model decision logic aligns with clinical reasoning, thereby limiting clinical adoption. This present study evaluates the interpretability of three rule-based machine learning classifiers—Decision Tree, RIPPER, and Rough Set—for MI detection from ECG signals, including a comparison between models with and without feature selection. Interpretability of the system is assessed through rule complexity analysis and a standardized qualitative clinical validation protocol involving three cardiologists, based on contemporary AHA/ESC ECG diagnostic guidelines. The findings indicate that the Rough Set classifier attains the optimal overall performance, with 80% of its generated rules demonstrating clinically aligned, thereby outperforming the other models regarding interpretability. The findings demonstrate the benefit of guideline-based clinical validation for advancing trustworthy ECG-based MI diagnostic systems.

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How to Cite
Fityah, F., Setiawan, N. A., & Anggrahini, D. W. (2025). Interpretability Evaluation of Rule-Based Classifier in Myocardial Infarction Classification Based on Syntactical Features of ECG Signal. Communications in Science and Technology, 10(2), 460–466. https://doi.org/10.21924/cst.10.2.2025.1851
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Vol. 10 No. 2 (2025)
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Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

1. World Heart Federation, World Heart Report 2023: Confronting the World’s Number One Killer, World Heart Federation, 2023, pp. 1–52.
2. N. Azizah, Kemenkes: Penyakit kardiovaskular jadi penyebab kematian terbanyak di Indonesia, Republika News, 2023.
3. N. Ojha, A.S. Dhamoon, Myocardial infarction, StatPearls Publishing, 2023.
4. N. Strodthoff, C. Strodthoff, Detecting and interpreting myocardial infarction using fully convolutional neural networks, Physiol. Meas. 40 (2019) 1–11.
5. S. Śmigiel, ECG classification using orthogonal matching pursuit and machine learning, Sensors 22 (2022).
6. S.M. Salerno, P.C. Alguire, H.S. Waxman, Competency in interpretation of 12-lead electrocardiograms: a summary and appraisal of published evidence, Ann. Intern. Med. (2003) 43–45.
7. P. Lu et al., KecNet: a light neural network for arrhythmia classification based on knowledge reinforcement, J. Healthc. Eng. (2021).
8. S. Hasbullah, M.S. Mohd Zahid, S. Mandala, Detection of myocardial infarction using hybrid models of convolutional neural network and recurrent neural network, BioMedInformatics 3 (2023) 478–492.
9. S. Aziz, S. Ahmed, M.S. Alouini, ECG-based machine-learning algorithms for heartbeat classification, Sci. Rep. 11 (2021) 1–14.
10. A.M. Sajiah, N.A. Setiawan, O. Wahyunggoro, Interval type-2 fuzzy logic system for diagnosis coronary artery disease, J. Telecommun. Electron. Comput. Eng. 1 (2016) 51–55.
11. J. Amann, A. Blasimme, E. Vayena, D. Frey, V.I. Madai, Explainability for artificial intelligence in healthcare: a multidisciplinary perspective, BMC Med. Inform. Decis. Mak. 20 (2020) 1–9.
12. A.J. London, Artificial intelligence and black-box medical decisions: accuracy versus explainability, Hastings Cent. Rep. 49 (2019) 15–21.
13. A. Vellido, The importance of interpretability and visualization in machine learning for applications in medicine and health care, Neural Comput. Appl. 32 (2020) 18069–18083.
14. C. Molnar, Interpretable machine learning: a guide for making black box models explainable, Creative Commons License, 2021.
15. H. Turbé, M. Bjelogrlic, C. Lovis, G. Mengaldo, Evaluation of post-hoc interpretability methods in time-series classification, Nat. Mach. Intell. 5 (2023) 250–260.
16. N. Alangari, M.E.B. Menai, H. Mathkour, I. Almosallam, Exploring evaluation methods for interpretable machine learning: a survey, Information 14 (2023) 1–29.
17. P. Wagner et al., PTB-XL, a large publicly available electrocardiography dataset, Sci. Data 7 (2020) 1–15.
18. W. Jenkal et al., An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform, Biocybern. Biomed. Eng. 36 (2016) 499–508.
19. M. AlMahamdy, H.B. Riley, Performance study of different denoising methods for ECG signals, Procedia Comput. Sci. 37 (2014) 325–332.
20. S. Śmigiel, K. Pałczyński, D. Ledziński, Deep learning techniques in the classification of ECG signals using R-peak detection based on the PTB-XL dataset, Sensors 21 (2021) 1–18.
21. L. De Palma et al., ECG wave segmentation algorithm for complete P-QRS-T detection, Proc. IEEE Int. Symp. Med. Meas. Appl. (2023) 1–6.
22. Y. Sattar, L. Chhabra, Electrocardiogram, StatPearls Publishing, 2023.
23. C. Hodnesdal et al., Rapidly upsloping ST-segment on exercise ECG: a marker of reduced coronary heart disease mortality risk, Eur. J. Prev. Cardiol. 20 (2013) 541–548.
24. W.J. Brady, ST segment and T wave abnormalities not caused by acute coronary syndromes, Emerg. Med. Clin. North Am. 24 (2006) 91–111.
25. J. Han, M. Kamber, J. Pei, Data mining: concepts and techniques, Elsevier, 2011.
26. S. Fletcher, M.Z. Islam, Decision tree classification with differential privacy: a survey, ACM Comput. Surv. 52 (2019) 1–35.
27. W.W. Cohen, Fast effective rule induction, Morgan Kaufmann, 1995.
28. Z. Pawlak, Rough sets: theoretical aspects of reasoning about data, Kluwer Academic Publishers, 1991.
29. A. Rakotomamonjy, Optimizing area under ROC curve with SVMs, Proc. ICML (2004) 71–80.
30. X. Chen, J.C. Jeong, Enhanced recursive feature elimination, Proc. ICMLA (2007) 429–435.
31. A. Task et al., 2023 ESC guidelines for the management of acute coronary syndromes, Eur. Heart J. (2023) 3720–3826.
32. M.H. Crawford et al., ACC/AHA guidelines for ambulatory electrocardiography, J. Am. Coll. Cardiol. 34 (1999).
33. M. Ciocirlan, A. Udrea, Techniques of biological signals classification and comparisons using machine learning techniques, Proc. CSCS (2023) 467–472.
34. N.A. Setiawan et al., Classification of arrhythmia ECG signal using cascade transparent classifier, J. Intell. Fuzzy Syst. 42 (2022) 1015–1025.
35. S. Singh, Comparative study ID3, CART and C4.5 decision tree algorithm: a survey, Int. J. Adv. Inf. Sci. Technol. 27 (2014) 97–103.
36. B. Halder, S. Mitra, M. Mitra, Classification of complete myocardial infarction using rule-based rough set method and rough set explorer system, IETE J. Res. 68 (2022) 85–95.
37. K.P. Ahmed, D.P. Acharjya, A hybrid scheme for heart disease diagnosis using rough set and cuckoo search technique, J. Med. Syst. 44 (2020).