Document Type : Original/Review Paper


1 Department of Computer Engineering, Rahbord Shomal Institute of Higher Education, Rasht, Iran.

2 Department of Health Informatics, Guilan Road Trauma Research Center, Trauma Institute, Guilan University of medical sciences, Rasht, Iran.

3 Department of Computer Engineering, Faculty of Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran.


Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the 5th most common cancer worldwide. Therefore, predicting the survival of patients, checking their health status, and detecting their risk of gastric cancer in the early stages can be very beneficial. Surprisingly, with the help of machine learning methods, this can be possible without the need for any invasive methods which can be useful for both patients and physicians in making informed decisions. Accordingly, a new hybrid machine learning-based method for detecting the risk of gastric cancer is proposed in this paper. The proposed model is compared with traditional methods and based on the empirical results, not only the proposed method outperform existing methods with an accuracy of 98% but also gastric cancer can be one of the most important consequences of H. pylori infection. Additionally, it can be concluded that lifestyle and dietary factors can heighten the risk of gastric cancer, especially among individuals who frequently consume fried foods and suffer from chronic atrophic gastritis and stomach ulcers. This risk is further exacerbated in individuals with limited fruit and vegetable intake and high salt consumption.


Main Subjects

  • Hashemi Amin, M. Ghaemi, SM. Mostafavi, L. Goshayeshi, K. Rezaei, M. Vahed, and B. Kiani , "A Geospatial database of gastric cancer patients and associated potential risk factors including lifestyle and air pollution, "BMC Research Notes, vol. 14, pp. 1-3, 2021.
  • Arbyn, E. Weiderpass, L. Bruni, S. De Sanjosé, M. Saraiya, J. Ferlay, F.Bray, "Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis,"The Lancet Global Health, vol. 9, no. 2, pp. e191-e203, 2020.
  • Samavat and E. Hojatzadeh, "Programs for prevention and control of cardiovascular diseases," Ministery of Health. Tehran: Javan, 2012.
  • Kalan Farmanfarma, N. Mahdavifar, S. Hassanipour, and H. Salehiniya, "Epidemiologic study of gastric cancer in Iran: a systematic review," Clinical and experimental gastroenterology, pp. 511-542, 2020.
  • J.Choi, J.H. Lee , Y.I. Kim, C.G. Kim, S.J. Cho, J.Y. Lee, K.W. Ryu, B.H. Nam, M.C.Kook, and Y.W. Kim, "Long-term outcome comparison of endoscopic resection and surgery in early gastric cancer meeting the absolute indication for endoscopic resection," Gastrointestinal endoscopy, vol. 81, no. 2, pp. 333-341, 2015.
  • Fukunaga, Y. Nagami, M. Shiba, M.Ominami, T.Tanigawa, H.Yamagami, H.Tanaka, K. Muguruma, T. Watanabe, K. Tominaga, and Y. Fujiwara, "Long-term prognosis of expanded-indication differentiated-type early gastric cancer treated with endoscopic submucosal dissection or surgery using propensity score analysis," Gastrointestinal endoscopy, vol. 85, no. 2, pp. 143-152, 2017.
  • Khodaverdian, H. Sadr, S.A. Edalatpanah, and M. Nazari , "An energy aware resource allocation based on combination of CNN and GRU for virtual machine selection," Multimedia Tools and Applications, pp. 1-28, 2023.
  • P.Kalashami, M.M. Pedram and H. Sadr," EEG feature extraction and data augmentation in emotion recognition,"Computational Intelligence and Neuroscience, 2022.
  • Khodaverdian, H. Sadr, M. Nazari, and S.A. Edalatpanah, " Predicting the workload of virtual machines in order to reduce energy consumption in cloud data centers using the combination of deep learning models," Journal of Information and Communication Technology, vol. 55, no. 5, pp. 158- 63, 2023.
  • Rejaul, I. Royel, M.A. Jaman, F. Masud, A. Ahmed, and A. Muyeed, "Machine learning and data mining methods in early detection of stomach cancer risk," Journal of Applied Science and Engineering, vol.24, no.5, pp. 1-8, 2021.
  • Li, A. Feng, S. Zheng, C. Chen, and J. Lyu, "Recent estimates and predictions of 5-year survival in patients with gastric cancer: A model-based period analysis," Cancer Control, vol. 29, 2022.
  • R. Afrash, M. Shafiee, and H. Kazemi-Arpanahi, "Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors," BMC gastroenterology, vol. 23, no. 1, pp. 1-13, 2023.
  • Roostaee, and R. Meidanshahi, "Hidden Patterns Discovery on Clinical Data: An Approach based on Data Mining Techniques," Journal of AI and Data Mining, 2023.
  • Brownlee, "Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end," Machine Learning Mastery, 2016.
  • Mishra, A. Biancolillo, J.M. Roger, F. Marini, and D.N. Rutledge, "New data preprocessing trends based on ensemble of multiple preprocessing techniques," TrAC Trends in Analytical Chemistry, vol. 132, 2020.
  • V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, vol. 16,  pp. 321-357, 2002.
  • Nakamura, Y. Kajiwara, A. Otsuka, and H. Kimura, "Lvq-smote–learning vector quantization based synthetic minority over–sampling technique for biomedical data," BioData mining, vol. 6, no.1, pp. 1-10, 2013.
  • Sadr, M. Nazari, M.M. Pedram, and M. Teshnehlab, "Exploring the efficiency of topic-based models in computing semantic relatedness of geographic terms." International journal of web research, vol.2 , no. 2, pp. 23-35, 2019.
  • Mohades Deilami, H. Sadr and M. Tarkhan, "Contextualized multidimensional personality recognition using combination of deep neural network and ensemble learning," Neural Processing Letters, vol. 54, no. 5, pp. 3811-3828, 2022
  • Yin and K. Gai. "An empirical study on preprocessing high-dimensional class-imbalanced data for classification," in 2015 IEEE 17th international conference on high performance computing and communications, 2015 IEEE 7th international symposium on cyberspace safety and security, and 2015 IEEE 12th international conference on embedded software and systems, IEEE, 2015.
  • K. Shukla, S.K. Pippal, S. Gupta, B. Ramachandra Reddy, and D. Tripathi, "Knowledge discovery in medical and biological datasets by integration of Relief-F and correlation feature selection techniques," Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6637-6648,  2020.
  • D. Nguyen, P.C. Roussis, B.T. Pham, M. Ferentinou, A. Mamou, D.Q. Vu, Q.A.T. Bui , D.K. Trong, and P.G. Asteris , "Bagging and multilayer perceptron hybrid intelligence models predicting the swelling potential of soil," Transportation Geotechnics, vol. 36, 2022.























  • Khodaverdian , H. Sadr, and S.A. Edalatpanah. "A shallow deep neural network for selection of migration candidate virtual machines to reduce energy consumption," in 2021 7th International conference on web research (ICWR), IEEE, 2021.
  • Bellili, M. Gilloux, and P. Gallinari, "An MLP-SVM combination architecture for offline handwritten digit recognition: Reduction of recognition errors by Support Vector Machines rejection mechanisms," Document Analysis and Recognition,vol. 5, no. 4 pp. 244-252, 2003.
  • M. Awad, R. Khanna, M. Awad, and R. Khanna, "Support vector machines for classification," Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, pp. 39-66, 2015
  • Sadr, M.M. Pedram, and M. Teshnehlab , 2021. "Convolutional neural network equipped with attention mechanism and transfer learning for enhancing performance of sentiment analysis", Journal of AI and data mining, vol. 9, no. 2, pp.141-151, 2021.