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An ensemble method of the machine learning to prognosticate the gastric cancer

Author

Listed:
  • Hirad Baradaran Rezaei

    (Amirkabir University of Technology)

  • Alireza Amjadian

    (Kharazmi University)

  • Mohammad Vahid Sebt

    (Kharazmi University)

  • Reza Askari

    (Kharazmi University)

  • Abolfazl Gharaei

    (Kharazmi University)

Abstract

Gastric Cancer is the most common malignancy of the digestive tract, which is the third leading cause of cancer-related mortality worldwide. The early prognosis methods, especially Machine Learning (ML)-based approaches are one of the main strategies against GC, which have become a necessity to identify and prognosticate the factors that affect the GC. They enable the specialists to accelerate the subsequent clinical management of patients, who suffer from the GC. This paper aims at creating an Ensemble Method inspired from ML to predict the most significant factors of the GC occurrence. The main objective of this research is to predict the probabilities of the GC occurrence and its associated deaths. To achieve this goal, the created EM benefits from some ML-based methods, including Least Absolute Shrinkage and Selection Operator (LASSO)/Ridge Regression, Elastic Net, Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decision Trees (GBDTs), and Deep Neural Network (DNN). The purpose of the provided EM is to lessen the prediction errors for the large number of the patients’ features. The main novelties of this research include: (i) A sequential EM created by a Stacking method to predict the probability of the GC and associated deaths; (ii) Benefiting from the significance level to make an accurate prediction; (iii) Employing two Chi-square tests to select the influent features; (iv) Tuning the parameters of the applied ML models to avoid over-fitting and intensifying the errors; (v) Applying different kinds of the regression methods to treat hyper-dimension cases; (vi) A new model for weighting the applied ML models. The outcomes of the implementation of the created EM in seven pioneer hospitals in the field of GC show that the designed EM generates more precise predictions with an accuracy of 97.9% and 76.3% to predict the GC and its associated deaths, respectively. Moreover, the obtained results from the Area Under Curve (AUC) validates and confirms the capability of the created EM to predict the probability of the GC and its related deaths with an accuracy of 98% and 90% to predict the GC and its associated deaths, respectively.

Suggested Citation

  • Hirad Baradaran Rezaei & Alireza Amjadian & Mohammad Vahid Sebt & Reza Askari & Abolfazl Gharaei, 2023. "An ensemble method of the machine learning to prognosticate the gastric cancer," Annals of Operations Research, Springer, vol. 328(1), pages 151-192, September.
    • Handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-04964-1
    DOI: 10.1007/s10479-022-04964-1
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    References listed on IDEAS

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    1. Mizuho Nishio & Mitsuo Nishizawa & Osamu Sugiyama & Ryosuke Kojima & Masahiro Yakami & Tomohiro Kuroda & Kaori Togashi, 2018. "Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-13, April.
    2. Ata Allah Taleizadeh & Aria Zaker Safaei & Arijit Bhattacharya & Alireza Amjadian, 2022. "Online peer-to-peer lending platform and supply chain finance decisions and strategies," Annals of Operations Research, Springer, vol. 315(1), pages 397-427, August.
    3. Abraham Pouliakis & Periklis Foukas & Konstantinos Triantafyllou & Niki Margari & Efrossyni Karakitsou & Vasileia Damaskou & Nektarios Koufopoulos & Tsakiraki Zoi & Martha Nifora & Alina-Roxani Goulou, 2020. "Machine Learning for Gastric Cancer Detection: A Logistic Regression Approach," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 9(2), pages 48-58, April.
    4. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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