Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Notes

  1. OLGA and OLGIM are grading and staging standards, which depend on the histopathology findings of gastroscopic biopsy sampling of cancer.

  2. The CCI is a method of categorizing comorbidities of patients based on the International Classification of Diseases (ICD).

References

  • Ajani, J. A., D’Amico, T. A., Almhanna, K., Bentrem, D. J., Chao, J., Das, P., & Sundar, H. (2016). Gastric cancer, version 32016, NCCN clinical practice guidelines in oncology. Journal of the National Comprehensive Cancer Network, 14(10), 1286–1312.

    Google Scholar 

  • Akcay, M., Etiz, D., & Celik, O. (2020). Prediction of survival and recurrence patterns by machine learning in gastric cancer cases undergoing radiation therapy and chemotherapy. Advances in Radiation Oncology, 5(6), 1179–1187.

    Google Scholar 

  • Amjadian, A., & Gharaei, A. (2021). An integrated reliable five-level closed-loop supply chain with multi-stage products under quality control and Green policies: Generalised outer approximation with exact penalty. International Journal of Systems Science: Operations & Logistics, 1–21.

  • Arai, J., Aoki, T., Sato, M., Niikura, R., Suzuki, N., Ishibashi, R., & Fujishiro, M. (2022). Machine learning–based personalized prediction of gastric cancer incidence using the endoscopic and histologic findings at the initial endoscopy. Gastrointestinal Endoscopy, 95(5), 864–872.

    Google Scholar 

  • Askari, R., Sebt, M. V., & Amjadian, A. (2020). A multi-product EPQ model for defective production and inspection with single machine, and operational constraints: Stochastic programming approach. International conference on logistics and supply chain management (pp. 161–193). Springer.

    Google Scholar 

  • Beraha, M., Metelli, A. M., Papini, M., Tirinzoni, A., & Restelli, M. (2019). Feature selection via mutual information: New theoretical insights. 2019 international joint conference on neural networks (IJCNN) (pp. 1–9). IEEE.

    Google Scholar 

  • Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA a Cancer Journal for Clinicians, 68(6), 394–424.

    Google Scholar 

  • Chen, M., & Decary, M. (2020). Artificial intelligence in healthcare: An essential guide for health leaders. Healthcare management forum (pp. 10–18). SAGE Publications.

    Google Scholar 

  • DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 837–845.

  • El-Manzalawy, Y., Hsieh, T. Y., Shivakumar, M., Kim, D., & Honavar, V. (2018). Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data. BMC Medical Genomics, 11(3), 19–31.

    Google Scholar 

  • Figueroa, R. L., Zeng-Treitler, Q., Kandula, S., & Ngo, L. H. (2012). Predicting sample size required for classification performance. BMC Medical Informatics and Decision Making, 12(1), 1–10.

    Google Scholar 

  • Gao, Y., Wang, H., Guo, M., & Li, Y. (2020). An adaptive machine learning pipeline for predicting the recurrence of gastric cancer. 2020 5th international conference on information science, computer technology and transportation (ISCTT) (pp. 408–411). IEEE.

    Google Scholar 

  • Gao, C., Sun, H., Wang, T., Tang, M., Bohnen, N. I., Müller, M. L., & Dinov, I. D. (2018). Model-based and model-free machine learning techniques for diagnostic prediction and classification of clinical outcomes in Parkinson’s disease. Scientific Reports, 8(1), 1–21.

    Google Scholar 

  • Gharaei, A., Karimi, M., & Hoseini Shekarabi, S. A. (2021c). Vendor-managed inventory for joint replenishment planning in the integrated qualitative supply chains: generalised benders decomposition under separability approach. International Journal of Systems Science: Operations & Logistics, 1–15.

  • Gharaei, A., Amjadian, A., & Shavandi, A. (2021a). An integrated reliable four-level supply chain with multi-stage products under shortage and stochastic constraints. International Journal of Systems Science: Operations & Logistics, 1–22.

  • Gharaei, A., Hoseini Shekarabi, S. A., & Karimi, M. (2021b). Optimal lot-sizing of an integrated EPQ model with partial backorders and re-workable products: An outer approximation. International Journal of Systems Science: Operations & Logistics, 1–17.

  • Gharaei, A., Amjadian, A., Amjadian, A., Shavandi, A., Hashemi, A., Taher, M., & Mohamadi, N. (2022). An integrated lot-sizing policy for the inventory management of constrained multi-level supply chains: null-space method. International Journal of Systems Science: Operations & Logistics, 1–14.

  • Gharaei, A., & Almehdawe, E. (2021). Optimal sustainable order quantities for growing items. Journal of Cleaner Production, 307, 127216.

    Google Scholar 

  • Gong, C., Zhou, M., Hu, Y., Ren, Z., Ren, J., & Yao, M. (2022). Elastic net-based identification of GAMT as potential diagnostic marker for early-stage gastric cancer. Biochemical and Biophysical Research Communications, 591, 7–12.

    Google Scholar 

  • Hayward, J., Alvarez, S. A., Ruiz, C., Sullivan, M., Tseng, J., & Whalen, G. (2010). Machine learning of clinical performance in a pancreatic cancer database. Artificial Intelligence in Medicine, 49(3), 187–195.

    Google Scholar 

  • Hinton, D. J., Vázquez, M. S., Geske, J. R., Hitschfeld, M. J., Ho, A., Karpyak, V. M., & Choi, D. S. (2017). Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects. Scientific Reports, 7(1), 1–8.

    Google Scholar 

  • Hosseinnataj, A., RezaBaneshi, M., & Bahrampour, A. (2020). Mortality risk factors in patients with gastric cancer using Bayesian and ordinary Lasso logistic models: A study in the Southeast of Iran. Gastroenterology and Hepatology from Bed to Bench, 13(1), 31.

    Google Scholar 

  • Hu, Y., Zhao, L., Li, Z., Dong, X., Xu, T., & Zhao, Y. (2022). Classifying the multi-omics data of gastric cancer using a deep feature selection method. Expert Systems with Applications, 200, 116813.

    Google Scholar 

  • Huang, R. J., Kwon, N. S. E., Tomizawa, Y., Choi, A. Y., Hernandez-Boussard, T., & Hwang, J. H. (2022). A comparison of logistic regression against machine learning algorithms for gastric cancer risk prediction within real-world clinical data streams. JCO Clinical Cancer Informatics., 6, 1015–1023.

    Google Scholar 

  • Li, C., Shi, C., Zhang, H., Chen, Y., & Zhang, S. (2015). Multiple instance learning for computer aided detection and diagnosis of gastric cancer with dual-energy CT imaging. Journal of Biomedical Informatics, 57, 358–368.

    Google Scholar 

  • Liu, R., Zhang, G., & Yang, Z. (2019). Towards rapid prediction of drug-resistant cancer cell phenotypes: Single cell mass spectrometry combined with machine learning. Chemical Communications, 55(5), 616–619.

    Google Scholar 

  • Maeta, K., Nishiyama, Y., Fujibayashi, K., Gunji, T., Sasabe, N., Iijima, K., & Naito, T. (2018). Prediction of glucose metabolism disorder risk using a machine learning algorithm: Pilot study. JMIR Diabetes, 3(4), e10212.

    Google Scholar 

  • McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23(2), 143–149.

    Google Scholar 

  • Meier, A., Nekolla, K., Earle, S., Hewitt, L., Aoyama, T., Yoshikawa, T., & Grabsch, H. I. (2018). End-to-end learning to predict survival in patients with gastric cancer using convolutional neural networks. Annals of Oncology, 29, viii23.

    Google Scholar 

  • Moghimi-Dehkordi, B., Safaee, A., & Tabei, S. Z. (2009). A comparison between Cox proportional hazard models and logistic regression on prognostic factors in gastric cancer. East African Journal of Public Health.

  • Mohindru, G., Mondal, K., Dutta, P., & Banka, H. (2022). Mining challenges in large-scale IoT data framework–a machine learning perspective. Advanced Data Mining Tools and Methods for Social Computing, 239–259.

  • Nishio, M., Nishizawa, M., Sugiyama, O., Kojima, R., Yakami, M., Kuroda, T., & Togashi, K. (2018). Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. PLoS ONE, 13(4), e0195875.

    Google Scholar 

  • Pouliakis, A., Foukas, P., Triantafyllou, K., Margari, N., Karakitsou, E., Damaskou, V., & Tzivras, M. (2020). Machine learning for gastric cancer detection: A logistic regression approach. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 9(2), 48–58.

    Google Scholar 

  • Qiao, Z., Sun, N., Li, X., Xia, E., Zhao, S., & Qin, Y. (2018). Using machine learning approaches for emergency room visit prediction based on electronic health record data. Building continents of knowledge in Oceans of data: The future of co-created eHealth (pp. 111–115). IOS Press.

    Google Scholar 

  • Ranstam, J., & Cook, J. A. (2018). LASSO regression. Journal of British Surgery, 105(10), 1348–1348.

    Google Scholar 

  • Shakeel, P. M., Tolba, A., Al-Makhadmeh, Z., & Jaber, M. M. (2020). Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks. Neural Computing and Applications, 32(3), 777–790.

    Google Scholar 

  • Sharma, H., Zerbe, N., Klempert, I., Hellwich, O., & Hufnagl, P. (2017). Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Computerized Medical Imaging and Graphics, 61, 2–13.

    Google Scholar 

  • Songun, I., Putter, H., Kranenbarg, E. M. K., Sasako, M., & van de Velde, C. J. (2010). Surgical treatment of gastric cancer: 15-year follow-up results of the randomised nationwide Dutch D1D2 trial. The Lancet Oncology, 11(5), 439–449.

    Google Scholar 

  • Souza, R. L. C., Ghasemi, A., Saif, A., & Gharaei, A. (2022). Robust job-shop scheduling under deterministic and stochastic unavailability constraints due to preventive and corrective maintenance. Computers & Industrial Engineering, 168, 108130.

    Google Scholar 

  • Taleizadeh, A. A., Noori-daryan, M., Soltani, M. R., & Askari, R. (2021). Optimal pricing and ordering digital goods under piracy using game theory. Annals of Operations Research, 1–38.

  • Taleizadeh, A. A., Safaei, A. Z., Bhattacharya, A., & Amjadian, A. (2022b). Online peer-to-peer lending platform and supply chain finance decisions and strategies. Annals of Operations Research, 1–31.

  • Taleizadeh, A. A., Askari, R., & Konstantaras, I. (2022). An optimization model for a manufacturing-inventory system with rework process based on failure severity under multiple constraints. Neural Computing and Applications, 34(6), 4221–4264.

    Google Scholar 

  • Taninaga, J., Nishiyama, Y., Fujibayashi, K., Gunji, T., Sasabe, N., Iijima, K., & Naito, T. (2019). Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study. Scientific Reports, 9(1), 1–9.

    Google Scholar 

  • Van Der Sommen, F., Zinger, S., Schoon, E. J., & De With, P. H. (2014). Supportive automatic annotation of early esophageal cancer using local gabor and color features. Neurocomputing, 144, 92–106.

    Google Scholar 

  • Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24(1), 175–186.

    Google Scholar 

  • Wang, G., & Qiao, J. (2021). An efficient self-organizing deep fuzzy neural network for nonlinear system modeling. IEEE Transactions on Fuzzy Systems.

  • Wang, G., Jia, Q. S., Zhou, M., Bi, J., Qiao, J., & Abusorrah, A. (2021). Artificial neural networks for water quality soft-sensing in wastewater treatment: a review. Artificial Intelligence Review, 1–23.

  • Wang, G., Bi, J., Jia, Q. S., Qiao, J., & Wang, L. (2022). Event-Driven Model Predictive Control with Deep Learning for Wastewater Treatment Process. IEEE Transactions on Industrial Informatics.

  • Wang, H., Zhou, X. B., Zhou, Y. B., Niu, Z. J., Chen, D., Wang, D. S., & Li, Y. (2008). Multivariate logistic regression analysis of postoperative severe complications and discriminant model establishment in gastric cancer post gastrectomy. Chinese Journal of Surgery, 46(24), 1902–1905.

    Google Scholar 

  • Warkentin, M., Sugumaran, V., & Sainsbury, R. (2012). The role of intelligent agents and data mining in electronic partnership management. Expert Systems with Applications, 39(18), 13277–13288.

    Google Scholar 

  • Wilke, H., Muro, K., Van Cutsem, E., Oh, S. C., Bodoky, G., Shimada, Y., & Ohtsu, A. (2014). Ramucirumab plus paclitaxel versus placebo plus paclitaxel in patients with previously treated advanced gastric or gastro-oesophageal junction adenocarcinoma (RAINBOW): A double-blind, randomised phase 3 trial. The Lancet Oncology, 15(11), 1224–1235.

    Google Scholar 

  • Wu, X., Tang, H., Guan, A., Sun, F., Wang, H., & Shu, J. (2016). Finding gastric cancer related genes and clinical biomarkers for detection based on gene–gene interaction network. Mathematical Biosciences, 276, 1–7.

    Google Scholar 

  • Xiao, Y., Wu, J., Lin, Z., & Zhao, X. (2018). A deep learning-based multi-model ensemble method for cancer prediction. Computer Methods and Programs in Biomedicine, 153, 1–9.

    Google Scholar 

  • Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.

    Google Scholar 

  • Zhang, Z., He, T., Huang, L., Li, J., & Wang, P. (2021). Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system. Computational and Structural Biotechnology Journal, 19, 2329–2346.

    Google Scholar 

  • Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (statistical Methodology), 67(2), 301–320.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

    Hirad Baradaran Rezaei

  2. Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Alireza Amjadian, Mohammad Vahid Sebt, Reza Askari & Abolfazl Gharaei

Authors
  1. Hirad Baradaran Rezaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Amjadian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Vahid Sebt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Reza Askari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abolfazl Gharaei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Vahid Sebt.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baradaran Rezaei, H., Amjadian, A., Sebt, M.V. et al. An ensemble method of the machine learning to prognosticate the gastric cancer. Ann Oper Res 328, 151–192 (2023). https://doi.org/10.1007/s10479-022-04964-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-022-04964-1

Keywords

Search

Navigation