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INDONESIA
Journal of Intelligent Systems Technology and Informatics
Published by Aliansi Peneliti Informatika
ISSN : -     EISSN : 3109757X     DOI : https://doi.org/10.64878/jistics
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
The Journal of Intelligent Systems Technology and Informatics (JISTICS) is an international peer-reviewed open-access journal that publishes high-quality research in the fields of Artificial Intelligence, Intelligent Systems, Information Technology, Computer Science, and Informatics. JISTICS aims to foster global scientific exchange by providing a platform for researchers, practitioners, and academics to disseminate original findings, critical reviews, and innovative applications. The journal is published three times a year (March, July, November) and may also publish special issues on emerging topics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 31 Documents
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An Explainable Graph Neural Network Framework for Anemia Classification on Imbalanced Tabular Data Moh Ramdani; Alya Rahmawati; Muhamad Rijki Nurjakiah
Journal of Intelligent Systems Technology and Informatics Vol 2 No 2 (2026): JISTICS, July 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i2.266

Abstract

Anemia remains a serious global health problem, yet its classification from hematological data faces issues of class imbalance and clinical interpretability. In this study, we propose an explainable Graph Neural Network (GCN) framework integrated with Synthetic Minority Over-sampling Technique (SMOTE) and GNNExplainer for anemia classification. Addressing the limitations of the previous global similarity threshold, we construct a localized k-nearest neighbors (k-NN) graph with k=5 and apply clinical-range filtering to remove physiologically impossible outliers. Evaluation on 136 test samples shows that the proposed GCN model achieves an accuracy of 91.91% and a recall of 82.76% for the anemia class, which is highly competitive with SVM RBF (93.38% accuracy) and Naive Bayes (91.91% accuracy). McNemar significance tests confirm that the GCN model performs comparably to the baselines (p >= 0.72) while offering model interpretability. Using GNNExplainer, the parameters MCH, Ht, HB, and MCHC were identified as the most dominant features, providing clinicians with valuable transparency. This framework demonstrates the clinical utility of explainable GNNs in medical diagnostics.

Page 4 of 4 | Total Record : 31

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