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A framework to predict second primary lung cancer patients by using ensemble models

Author

Listed:
  • Yen-Chun Huang

    (Tamkang University)

  • Chieh-Wen Ho

    (Department of Biology, Texas A&M University)

  • Wen-Ru Chou

    (Fu Jen Catholic University
    Fu Jen Catholic University)

  • Mingchih Chen

    (Fu Jen Catholic University
    Fu Jen Catholic University)

Abstract

Machine learning (ML) model prediction, which has been wildly used in healthcare industry recently, serves as a tool to help users to make quick decisions. The prediction results could improve treatment outcomes and reduce the medical expenses. This research proposed the ML-based decision tool to predict the second primary lung cancer probability within lung cancer patients. This tool included following stages: The first stage is data processing to select the target patients by using National Health Insurance Research Database from 2011 to 2016 period as study. The second stage has used synthetic minority oversampling technique (SMOTE) to make data balancing. The third stage is feature selecting, and in final stage, we have applied five ML algorithms, which is included: Logistic Regression (LGR), Decision Tree, Random Forests (RF), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) with optimal features, then followed by building ensemble models. The results show that after feature selection, the ensemble models yield an accuracy rate 0.932. Different types of therapy (Chemotherapy (CH); Radiotherapy (RT), tyrosine kinase inhibitor (TKI)), different clinical stages, and Epidermal Growth Factor Receptor (EGFR) states were the top five optimal features affecting developed second primary lung cancer. This study can help physicians to identify the possibility with second primary lung cancer patients and make complete treatment plans for them.

Suggested Citation

  • Yen-Chun Huang & Chieh-Wen Ho & Wen-Ru Chou & Mingchih Chen, 2025. "A framework to predict second primary lung cancer patients by using ensemble models," Annals of Operations Research, Springer, vol. 348(1), pages 373-397, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-023-05691-x
    DOI: 10.1007/s10479-023-05691-x
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    References listed on IDEAS

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    1. Gregorutti, Baptiste & Michel, Bertrand & Saint-Pierre, Philippe, 2015. "Grouped variable importance with random forests and application to multiple functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 15-35.
    2. repec:plo:pone00:0048528 is not listed on IDEAS
    3. Talayeh Razzaghi & Ilya Safro & Joseph Ewing & Ehsan Sadrfaridpour & John D. Scott, 2019. "Predictive models for bariatric surgery risks with imbalanced medical datasets," Annals of Operations Research, Springer, vol. 280(1), pages 1-18, September.
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