Interpretable Machine Learning with SHAP and XGBoost for Lung Cancer Prediction Insights

Interpretable Machine Learning with SHAP and XGBoost for Lung Cancer Prediction Insights

Kanker paru-paru tetap menjadi salah satu penyebab kematian utama di seluruh dunia, dan deteksi dini melalui metode yang akurat dan andal sangat penting untuk meningkatkan prognosis pasien. Studi ini mengusulkan model klasifikasi kanker paru-paru yang mengintegrasikan XGBoost dengan SHapley Additive...

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Bibliographic Details
Main Authors:	Taufik Kurniawan, Laily Hermawanti, Achmad Nuruddin Safriandono
Format:	Article
Language:	English
Published:	Politeknik Negeri Batam 2024-11-01
Series:	Journal of Applied Informatics and Computing
Subjects:	balancing dataset interpretable machine learning lung cancer classification shapley additive explanations xgboost
Online Access:	https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8395
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