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All Journal Majalah Ilmiah MOMENTUM TATAL JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Achmad Nuruddin Safriandono
Sultan Fatah University
Computer Science & IT Decision Sciences, Operations Research & Management Education

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Interpretable Machine Learning with SHAP and XGBoost for Lung Cancer Prediction Insights Kurniawan, Taufik; Hermawanti, Laily; Safriandono, Achmad Nuruddin
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8395

Abstract

Lung cancer remains one of the leading causes of death worldwide, and early detection through accurate and reliable methods is essential to improve patient prognosis. This study proposes a lung cancer classification model that integrates XGBoost with SHapley Additive exPlanations (SHAP) and Random Over Sampling (ROS) techniques to address the data imbalance problem. Using hyperparameter optimization through Optuna, the resulting model demonstrated superior performance, with an average accuracy of 96.84%, precision of 99.23%, recall of 94.51%, F1-score of 96.74%, specificity of 99.17%, and AUC of 96.84% in a 10-fold cross-validation evaluation. SHAP analysis provided significant interpretability, identifying key features such as gender, smoking habits, and physical signs of yellow fingers as the factors that most influence the model's predictions. The results of this study indicate that the proposed model is not only accurate, but also interpretable, making a significant contribution to supporting better clinical decision making in lung cancer diagnosis.
Co-Authors Akhmad Dahlan Budi, Setyo De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Farah Zakiyah Rahmanti Iwan Setiawan Wibisono Laily Hermawanti Laily Hermawanti Muslikh, Ahmad Rofiqul Mustofa, Fachrul Ojugo, Arnold Adimabua Suyud Widiono Taufik Kurniawan Warto -