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All Journal Journal of Nonformal Education JITRO (Jurnal Ilmiah dan Teknologi Peternakan Tropis) Publiciana TATAL AdBispreneur JOURNAL OF APPLIED INFORMATICS AND COMPUTING PRoMEDIA IQTISHADUNA: Jurnal Ekonomi dan Keuangan Islam Develop BUANA SAINS Prosiding Temu Profesi Tahunan PERHAPI Journal of Tourism Destination and Attraction Journal of Economics, Business, and Government Challenges Sinomika Journal: Publikasi Ilmiah Bidang Ekonomi dan Akuntansi Journal of Artificial Intelligence and Digital Business Journal of Economics, Business, Management, Accounting And Social Sciences AMMA : Jurnal Pengabdian Masyarakat JPMAT Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi TEKNOBIS : Jurnal Teknologi, Bisnis dan Pendidikan Fonologi: Jurnal Ilmuan Bahasa dan Sastra Inggris RISK : Jurnal Riset Bisnis dan Ekonomi Neraca Manajemen, Akuntansi, dan Ekonomi Bogor Hospitality Journal Kajian Ekonomi dan Akuntansi Terapan GRAFTA: Journal of Christian Religion Education and Biblical Studies Jurnal Gladiator Jurnal Ilmiah Pariwisata
Taufik Kurniawan
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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 , Dhiyan Septa Wihara Abdul Hamid Achmad Nuruddin Safriandono Adelia Pratiwi, Anggun Adrian Fahreza Sanjaya Ali Farhan ALMUBAROK, HUSNI DWI DHUHAWAN Amin Sadiqin Amrina Yulfajar Angelina, Michelle Anton Ariyanto Aulia, Lucky Rinandar Dwi Ayu Puspita Putri Wicaksono Bambang Edison Bayu Permadi Diah Pratiwi Eka Fitasari Eny Nur Aisyah Fadillah Fadillah Faridah, Abdatul Fariz Khadiqi, M. Feri Tiona Pasaribu Firdausi Amaliyah Gembul, Delsiana Ghoi, Maria Vianey Hardika Hardika, Hardika Hariani, Dina Herman Supriadi Hidajat, Koerniawan Hidayah, Sofiana Hidayat, Jorgi Adam Irwan Rahadi Jeki Haryanto Jumariah, Erma Kartika Kusmayati, Nindya Khusmasari, Jihan Kurniawan, Yuli kusmayati, Nindya Kusmayati, Nindya Kartika Kusuma Adi Rahardjo Laily Hermawanti Lulu, Magdalena Rika Masrun masrun Mochammad Mamduch Barsa Mohammad Faiz Karimy, Mohammad Faiz Muh Hilmi Pauzi Muslikhun Mutia, Rada Irma Nengseh, Sri Wahyuni Nindya Kartika Kusmayati Nugroho, Bagaskara Widi Purwo Subekti Purwoko, Andik Putri R, Diah Suci Rahman, Jhoni Rahmawati, Eka Septia Ramli Hidir Ria Kamilah Agustina Rinanti, Rosyida Fajri Santoso, Yudivan Ilham Setiyawan, Ahmad Iskandar Suganda sugiyanto sugiyanto Sutarmin Sutarmin, Sutarmin suwangsi, iwang Suwangsih, Iwang Taruna, Taruna Tria Gustiningsi Widiyanti, Opi Trimarita Wilenthino, M. Farel wiwik budiarti Wulandari, Lusi Wahyu Wulandari, Novianti Wulansari, Dhyah Yogi Setiawan Yuli Kurniawati Yuli Kurniawati, Yuli Yuviani Kusumawardhani Yuviani Kusumawardhani, Yuviani