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All Journal Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan EDUMATIC: Jurnal Pendidikan Informatika Progresif: Jurnal Ilmiah Komputer jurnal ilmiah hospitality INFORMASI (Jurnal Informatika dan Sistem Informasi) Jurnal Penelitian Ekonomi Akuntansi (JENSI) Jurnal Sistem Informasi dan Sains Teknologi Journal of Computer Networks, Architecture and High Performance Computing Journal of Innovation Research and Knowledge Nusantara : Jurnal Ilmiah Pariwisata dan Perhotelan Indonesian Community Service Journal of Computer Science (IndoComs) Manexia EVOLUSI : Jurnal Sains dan Manajemen
Taufik Wibisono
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Explainable Machine Learning Framework for Thyroid Cancer Recurrence Prediction Tuti Alawiyah; Taufik Wibisono; Recha Abriana Anggraini; Bambang Kelana Simpony; Yesti Siti Nurjanah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8894

Abstract

Accurate prediction of thyroid cancer recurrence is essential for improving long-term patient management and supporting evidence-based clinical decision-making. Although machine learning has demonstrated promising predictive performance, limited model interpretability remains a major barrier to its clinical adoption. This study aims to develop an Explainable Machine Learning framework for thyroid cancer recurrence prediction by integrating Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) using clinicopathological features. A publicly available dataset containing 383 patient records was preprocessed through label encoding, correlation analysis, Chi-Square-based feature selection, and Min-Max normalization. Logistic Regression, Decision Tree, Random Forest, and XGBoost were comparatively evaluated using 10-fold stratified cross-validation with Accuracy, Precision, Recall, F1-score, and ROC-AUC as evaluation metrics. The best-performing model was subsequently interpreted using global and local SHAP analyses. XGBoost achieved the highest performance, with an accuracy of 95.8% ± 4.4%, precision of 93.4% ± 8.3%, recall of 91.4% ± 9.9%, F1-score of 92.2% ± 8.3%, and ROC-AUC of 98.6% ± 2.5%, outperforming the other models. SHAP analysis identified Response, Risk, and N Stage as the most influential clinicopathological factors affecting recurrence prediction. This study contributes by developing a unified Explainable Machine Learning framework that integrates comparative model evaluation, XGBoost prediction, and global and local SHAP interpretation within a single workflow. The proposed framework provides accurate and clinically interpretable recurrence prediction, supporting trustworthy risk assessment and personalized decision-making in thyroid cancer management.
Co-Authors Agung Baitul Hikmah Ai Ilah Warnilah Alawiyah, Tuti Alawiyah, Tuti Alawiyah Amirulloh, Imam Apriyani, Yanti Bambang Kelana Simpony Firdaus, Reyhan Andrea Haerul Fatah Herlan Sutisna, Herlan Imam Amirulloh Imam Amirulloh Iskandar, Iqbal Dzulfiqar Kusmira, Mira Melisa Winda Pertiwi Mira Kusmira Nurjanah, Yesti Siti Ratningsih, Ratningsih Recha Abriana Anggraini Rezkiani Rezkiani Sidik, Zamzam Permana Maulana Syafira Anbar Roswita Tuti Alawiyah Tuti Yani Sri Mulyani Yanti Apriyani Yesti Siti Nurjanah Yesti Siti Nurjanah