Lung Cancer Classification Using Stacking Framework of BiLSTM, Logistic Regression, and XGBoost

Lung Cancer Classification Using Stacking Framework of BiLSTM, Logistic Regression, and XGBoost

Lung cancer remains one of the most prevalent and deadly cancers worldwide, causing over 1.8 million deaths each year. Early and accurate classification of lung cancer is crucial, yet existing machine learning and deep learning models often face limitations in generalization and reliability. To addr...

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Bibliographic Details
Main Authors: Muhammad Nikho, Dwi Putra, Zaenuddin, ., Silvia, Ratna, Haldi, Budiman, Erfan, Karyadiputra, Tri Wahyu, Qur’ana, Desy Ika, Puspitasari, Galih, Mahalisa, Nur, Arminarahmah
Format: Article
Language:English
English
Published: INTI International University 2025
Subjects:
QA75 Electronic computers. Computer science
RC Internal medicine
T Technology (General)
Online Access:http://eprints.intimal.edu.my/2178/
http://eprints.intimal.edu.my/2178/1/ij2025_29.pdf
http://eprints.intimal.edu.my/2178/2/727

Internet

http://eprints.intimal.edu.my/2178/
http://eprints.intimal.edu.my/2178/1/ij2025_29.pdf
http://eprints.intimal.edu.my/2178/2/727

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