An Interpretable Machine Learning Framework for Predicting Advanced Tumor Stages

Authors

  • Teuku Rizky Noviandy Department of Information Systems, Faculty of Engineering, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Mohsina Patwekar Department of Pharmacology, Luqman College of Pharmacy, Karnataka 585102, India
  • Faheem Patwekar Department of Pharmacognosy, Luqman College of Pharmacy, Karnataka 585102, India
  • Rinaldi Idroes School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/ijds.v3i2.364

Keywords:

Tumor stage prediction, Machine learning, Clinical prediction, Medical machine learning

Abstract

Accurate identification of advanced tumor stages is essential for timely clinical decision-making and personalized treatment planning. This study proposes an explainable ensemble learning framework for predicting advanced tumor stage using a dataset containing 10,000 samples with 18 clinical and radiological features. Four machine learning models, namely Logistic Regression, Naïve Bayes, AdaBoost, and LightGBM, were evaluated using stratified train–test splits along with standard performance metrics. LightGBM achieved the highest performance, with an accuracy of 86.05% and an F1-score of 76.61%, outperforming linear and probabilistic classifiers. ROC–AUC and precision–recall analyses further confirmed the superior discriminative ability of ensemble methods. SHAP explainability techniques highlighted mitotic count, Ki-67 index, enhancement, and necrosis as the most influential predictors of advanced stage. The proposed framework demonstrates strong predictive capability and provides clinically interpretable insights, underscoring its potential as a decision-support tool in oncological diagnostics. Future work will involve external validation and integration of additional multimodal data to enhance generalizability.

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Published

2025-11-29

How to Cite

Noviandy, T. R., Patwekar, M., Patwekar, F., & Idroes, R. (2025). An Interpretable Machine Learning Framework for Predicting Advanced Tumor Stages. Infolitika Journal of Data Science, 3(2), 61–69. https://doi.org/10.60084/ijds.v3i2.364

Issue

Vol. 3 No. 2 (2025): November 2025

Section

Articles

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Copyright (c) 2025 Teuku Rizky Noviandy, Mohsina Patwekar, Faheem Patwekar, Rinaldi Idroes

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.