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All Journal Communications in Science and Technology Malcom: Indonesian Journal of Machine Learning and Computer Science
Fityah, Farhatul
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Computer Science & IT Engineering

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Impact of Cover Parameter Value on Rule Generation in Rough Set Classification Fityah, Farhatul; Sofyan , Pramudya Rakhmadyansyah
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1831

Abstract

Machine learning plays a crucial role in healthcare classification, with Rough Set Theory (RST) offering effective tools for managing data uncertainty. Within RST, the RSES2 tool supports algorithms like LEM2 and Covering, yet the influence of cover parameter values on rule generalization and specificity remains underexplored. This study investigates these effects using the Differentiated Thyroid Cancer dataset. The research investigates the trade-offs between rule generalization and specificity by adjusting cover parameter settings, which dictate the minimum and maximum cases a rule must cover. The comparison reveals that the LEM2 algorithm maintains high accuracy across various cover parameter values, with only a slight decline as the parameter increases, and shows improved coverage with higher cover values. In contrast, the Covering algorithm displays greater fluctuations in accuracy, peaking at lower cover parameter values and decreasing significantly as the parameter rises. Coverage for the Covering algorithm is highest at lower cover parameters but decreases sharply at higher values. This indicates that LEM2 is more robust in maintaining accuracy and coverage, while the Covering algorithm performs better at lower cover parameters but struggles with stability as the parameter increases.
Interpretability Evaluation of Rule-Based Classifier in Myocardial Infarction Classification Based on Syntactical Features of ECG Signal Fityah, Farhatul; Setiawan, Noor Akhmad; Anggrahini, Dyah Wulan
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1851

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.
Co-Authors Anggrahini, Dyah Wulan Noor Akhmad Setiawan Sofyan , Pramudya Rakhmadyansyah