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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English English |
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INTI International University
2025
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| 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 |
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| author | Muhammad Nikho, Dwi Putra Zaenuddin, . Silvia, Ratna Haldi, Budiman Erfan, Karyadiputra Tri Wahyu, Qur’ana Desy Ika, Puspitasari Galih, Mahalisa Nur, Arminarahmah |
| author_facet | Muhammad Nikho, Dwi Putra Zaenuddin, . Silvia, Ratna Haldi, Budiman Erfan, Karyadiputra Tri Wahyu, Qur’ana Desy Ika, Puspitasari Galih, Mahalisa Nur, Arminarahmah |
| author_sort | Muhammad Nikho, Dwi Putra |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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 address this issue, this study proposes a stacking framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) and Logistic Regression as base learners, with Extreme Gradient Boosting (XGBoost) serving as the meta-learner. The rationale for this approach is that BiLSTM captures complex feature interactions, Logistic Regression provides interpretability, and XGBoost has demonstrated strong performance as a meta-learner in ensemble studies. The framework was evaluated on a publicly available lung cancer dataset consisting of 309 patient records with 15 clinical and lifestyle attributes. Experimental results showed that the stacking framework achieved perfect accuracy of 1.00, outperforming BiLSTM (0.95) and Logistic Regression (0.93). These findings confirm the effectiveness of the proposed ensemble in overcoming the weaknesses of individual models and highlight its novelty as a reliable approach for lung cancer classification |
| first_indexed | 2025-11-14T11:59:51Z |
| format | Article |
| id | intimal-2178 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:51Z |
| publishDate | 2025 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21782025-09-21T03:20:36Z http://eprints.intimal.edu.my/2178/ Lung Cancer Classification Using Stacking Framework of BiLSTM, Logistic Regression, and XGBoost Muhammad Nikho, Dwi Putra Zaenuddin, . Silvia, Ratna Haldi, Budiman Erfan, Karyadiputra Tri Wahyu, Qur’ana Desy Ika, Puspitasari Galih, Mahalisa Nur, Arminarahmah QA75 Electronic computers. Computer science RC Internal medicine T Technology (General) 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 address this issue, this study proposes a stacking framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) and Logistic Regression as base learners, with Extreme Gradient Boosting (XGBoost) serving as the meta-learner. The rationale for this approach is that BiLSTM captures complex feature interactions, Logistic Regression provides interpretability, and XGBoost has demonstrated strong performance as a meta-learner in ensemble studies. The framework was evaluated on a publicly available lung cancer dataset consisting of 309 patient records with 15 clinical and lifestyle attributes. Experimental results showed that the stacking framework achieved perfect accuracy of 1.00, outperforming BiLSTM (0.95) and Logistic Regression (0.93). These findings confirm the effectiveness of the proposed ensemble in overcoming the weaknesses of individual models and highlight its novelty as a reliable approach for lung cancer classification INTI International University 2025-09 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2178/1/ij2025_29.pdf text en cc_by_4 http://eprints.intimal.edu.my/2178/2/727 Muhammad Nikho, Dwi Putra and Zaenuddin, . and Silvia, Ratna and Haldi, Budiman and Erfan, Karyadiputra and Tri Wahyu, Qur’ana and Desy Ika, Puspitasari and Galih, Mahalisa and Nur, Arminarahmah (2025) Lung Cancer Classification Using Stacking Framework of BiLSTM, Logistic Regression, and XGBoost. INTI JOURNAL, 2025 (29). pp. 1-6. ISSN e2600-7320 https://intijournal.intimal.edu.my |
| spellingShingle | QA75 Electronic computers. Computer science RC Internal medicine T Technology (General) Muhammad Nikho, Dwi Putra Zaenuddin, . Silvia, Ratna Haldi, Budiman Erfan, Karyadiputra Tri Wahyu, Qur’ana Desy Ika, Puspitasari Galih, Mahalisa Nur, Arminarahmah Lung Cancer Classification Using Stacking Framework of BiLSTM, Logistic Regression, and XGBoost |
| title | Lung Cancer Classification Using Stacking Framework of BiLSTM,
Logistic Regression, and XGBoost |
| title_full | Lung Cancer Classification Using Stacking Framework of BiLSTM,
Logistic Regression, and XGBoost |
| title_fullStr | Lung Cancer Classification Using Stacking Framework of BiLSTM,
Logistic Regression, and XGBoost |
| title_full_unstemmed | Lung Cancer Classification Using Stacking Framework of BiLSTM,
Logistic Regression, and XGBoost |
| title_short | Lung Cancer Classification Using Stacking Framework of BiLSTM,
Logistic Regression, and XGBoost |
| title_sort | lung cancer classification using stacking framework of bilstm,
logistic regression, and xgboost |
| topic | QA75 Electronic computers. Computer science RC Internal medicine T Technology (General) |
| url | http://eprints.intimal.edu.my/2178/ http://eprints.intimal.edu.my/2178/ http://eprints.intimal.edu.my/2178/1/ij2025_29.pdf http://eprints.intimal.edu.my/2178/2/727 |