Diagnosis of Gastric Cancer Using Machine Learning Techniques in Healthcare Sector: A Survey

Danish Jamil


Many researchers are trying hard to minimize the incidence of cancers, especially GC. For GC, the five-year survival rate is generally 5–25%, but for EGC it can be reduced by up to 90%. Among the cancers, GC is more deadly. It is difficult for doctors to assess its threat to patients as it requires years of medical practice and rigorous testing. The health sector has benefitted from AI for the early diagnosis or classification of GC. However, the current AI-based techniques need to be further improved so they can be used in clinical testing. Heterogeneous GC characterization requires more optimized methods for early detection of GC because of its type and severity. Hence, it is important to further investigate this area and come up with more optimized approaches for early diagnosis. Early detection will increase the chances of successful treatments. In this study, we have conducted a literature survey detailing the role of AI in the healthcare sector for GC diagnosis. We discuss basic principles, advantages and disadvantages, training and testing of data, and integration of applications like DSS, CDSS, KDD, ML, DM, BD, and DL, and their relevance to the healthcare industry. The study focuses on the application of ML techniques used in the diagnosis of GC. This review paper also introduces DM techniques, how they are applied in the healthcare industry, their limitations, roles and, operational challenges. This will assist pathologists to help minimize their workload while increasing the diagnostic accuracy. These techniques will further assist medical practitioners with their decision-making process.

Full Text:




M. Mahmood, B. Al-Khateeb, and W. M. Alwash, “A review on neural networks approach on classifying cancers,” Int J Artif Intell, vol. 9, no. 2, pp. 317–326, 2020.

A. M. Brushfield, T. T. Luu, B. D. Callahan, and P. E. Gilbert, “A comparison of discrimination and reversal learning for olfactory and visual stimuli in aged rats.,” Behav. Neurosci., vol. 122, no. 1, p. 54, 2008.

R. A. Smith et al., “Cancer screening in the United States, 2019: A review of current American Cancer Society guidelines and current issues in cancer screening,” CA. Cancer J. Clin., vol. 69, no. 3, pp. 184–210, 2019.

A. Shetty and V. Shah, “Survey of Cervical Cancer Prediction Using Machine Learning: A Comparative Approach,” in 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2018, pp. 1–6.

A. M. Abdel-Zaher and A. M. Eldeib, “Breast cancer classification using deep belief networks,” Expert Syst. Appl., vol. 46, pp. 139–144, 2016.

R. Assari, P. Azimi, and M. R. Taghva, “Heart Disease Diagnosis Using Data Mining Techniques,” Int. J. Econ. Manag. Sci., vol. 6, no. 3, 2017.

U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI Mag., vol. 17, no. 3, p. 37, 1996.

P. Bertuccio et al., “Citrus fruit intake and gastric cancer: The stomach cancer pooling (StoP) project consortium,” Int. J. cancer, vol. 144, no. 12, pp. 2936–2944, 2019.

S. Secinaro, D. Calandra, A. Secinaro, V. Muthurangu, and P. Biancone, “The role of artificial intelligence in healthcare: a structured literature review,” BMC Med. Inform. Decis. Mak., vol. 21, no. 1, pp. 1–23, 2021.

C. J. Kelly, A. Karthikesalingam, M. Suleyman, G. Corrado, and D. King, “Key challenges for delivering clinical impact with artificial intelligence,” BMC Med., vol. 17, no. 1, pp. 1–9, 2019.

S. van Baalen, M. Boon, and P. Verhoef, “From clinical decision support to clinical reasoning support systems.,” Authorea Prepr., 2020.

S. Akundi, R. Soujanya, and P. M. Madhuri, “Big Data Analytics in Healthcare Using Machine Learning Algorithms: A Comparative Study,” 2020.

P. Acharya and M. Mathur, “Artificial intelligence in dermatology: the ‘unsupervised’learning,” Br. J. Dermatol., vol. 182, no. 6, pp. 1507–1508, 2020.

T. Silwattananusarn and K. Tuamsuk, “Data mining and its applications for knowledge management: a literature review from 2007 to 2012,” arXiv Prepr. arXiv1210.2872, 2012.

S. S. ZIA, P. AKHTAR, and T. J. A. MUGHAL, “Case Retrieval Process of CBR Technique Implements on Knowledge-Based Clinical Decision Support Systems (KBCDSS) for Diagnosis of Breast Cancer Disease,” Sindh Univ. Res. Journal-SURJ (Science Ser., vol. 47, no. 2, 2015.

A. Karahoca, Advances in data mining knowledge discovery and applications. BoD--Books on Demand, 2012.

P. E. Beeler, D. W. Bates, and B. L. Hug, “Clinical decision support systems,” Swiss Med. Wkly., vol. 144, p. w14073, 2014.

C. Schuh, J. S. de Bruin, and W. Seeling, “Clinical decision support systems at the Vienna General Hospital using Arden Syntax: Design, implementation, and integration,” Artif. Intell. Med., vol. 92, pp. 24–33, 2018.

J. Ferlay et al., “Cancer statistics for the year 2020: An overview,” Int. J. Cancer, 2021.

S. S. ZIA, P. Akhtar, and T. J. A. MUGHAL, “Schematic Cycle of Case-Based Reasoning Technique Implements in Clinical Decision Support Systems Used for Diagnosis of Liver Disease,”

Sindh Univ. Res. Journal-SURJ (Science Ser., vol. 47, no. 2, 2015.

T. Lysaght, H. Y. Lim, V. Xafis, and K. Y. Ngiam, “AI-assisted decision-making in healthcare,” Asian Bioeth. Rev., vol. 11, no. 3, pp. 299–314, 2019.

H. C. Koh, G. Tan, and others, “Data mining applications in healthcare,” J. Healthc. Inf. Manag., vol. 19, no. 2, p. 65, 2011.

I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I.

Vlahavas, and I. Chouvarda, “Machine learning and data mining methods in diabetes research,” Comput. Struct. Biotechnol. J., vol. 15, pp. 104–116, 2017.

C. Neto, M. Brito, V. Lopes, H. Peixoto, A. Abelha, and J. Machado, “Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients,” Entropy, vol. 21, no. 12, p. 1163, 2019, doi: 10.3390/e21121163.

F. M. Couto, Data and text processing for health and life sciences. Springer Nature, 2019.

T. Panch, P. Szolovits, and R. Atun, “Artificial intelligence, machine learning and health systems,” J. Glob. Health, vol. 8, no. 2, 2018.

S. R. Kumar, N. Gayathri, S. Muthuramalingam, B. Balamurugan, C. Ramesh, and M. K. Nallakaruppan, “Medical big data mining and processing in e-healthcare,” in Internet of Things in Biomedical Engineering, Elsevier, 2019, pp. 323–339.

M. Mittal, L. M. Goyal, D. J. Hemanth, and J. K. Sethi, “Clustering approaches for high-dimensional databases: A review,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 9, no. 3, p. e1300, 2019.

H. W. Ian and F. Eibe, “Data mining: Practical machine learning tools and techniques.” Morgan Kaufmann Publishers, 2005.

L. Wang and C. A. Alexander, “Big data analytics in medical engineering and healthcare: methods, advances, and challenges,” J. Med. Eng. & Technol., vol. 44, no. 6, pp. 267–283, 2020.

I. H. Witten, E. Frank, and M. A. Hall, Data Mining Practical Machine Learning Tools and Techniques Third Edition. Morgan Kaufmann, 2017.

K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V Karamouzis, and D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction,” Comput. Struct. Biotechnol. J., vol. 13, pp. 8–17, 2015.

N. Nissim et al., “Improving condition severity classification with an efficient active learning based framework,” J. Biomed. Inform., vol. 61, pp. 44–54, 2016.

N. Iqbal and M. Islam, “Machine learning for dengue outbreak prediction: A performance evaluation of different prominent classifiers,” Informatica, vol. 43, no. 3, 2019.

N. Nissim, Y. Shahar, Y. Elovici, G. Hripcsak, and R. Moskovitch, “Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods,” Artif. Intell. Med., vol. 81, pp. 12–32, 2017.

S. K. Zhou et al., “A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises,” arXiv Prepr. arXiv2008.09104, 2020.

T. Lu, Y. Du, L. Ouyang, Q. Chen, and X. Wang, “Android malware detection based on a hybrid deep learning model,” Secur. Commun. Networks, vol. 2020, 2020.

T. J. Saleem and M. A. Chishti, “Exploring the applications of Machine Learning in Healthcare,” Int. J. Sensors Wirel. Commun. Control, vol. 10, no. 4, pp. 458–472, 2020.

S. Mittal and Y. Hasija, “Applications of deep learning in healthcare and biomedicine,” in Deep Learning Techniques for Biomedical and Health Informatics, Springer, 2020, pp. 57–77.

Y.-W. Chen and L. C. Jain, “Deep Learning in Healthcare.” Springer, 2020.

S. Pitoglou, “Machine Learning in Healthcare: Introduction and Real-World Application Considerations,” in Quality Assurance in the Era of Individualized Medicine, IGI Global, 2020, pp. 92–109.

A. Mustafa and M. Rahimi Azghadi, “Automated Machine Learning for Healthcare and Clinical Notes Analysis,” Computers, vol. 10, no. 2, p. 24, 2021.

H. Wang and B. Raj, “A survey: Time travel in deep learning space: An introduction to deep learning models and how deep learning models evolved from the initial ideas,” arXiv Prepr. arXiv1510.04781, 2015.

A. Crippa et al., “Use of machine learning to identify children with autism and their motor abnormalities,” J. Autism Dev. Disord., vol. 45, no. 7, pp. 2146–2156, 2015.

A. D. Gavrilov, A. Jordache, M. Vasdani, and J. Deng, “Preventing model overfitting and underfitting in convolutional neural networks,” Int. J. Softw. Sci. Comput. Intell., vol. 10, no. 4, pp. 19–28, 2018.

P. Samui, Handbook of research on advanced computational techniques for simulation-based engineering. IGI Global, 2015.

X.-Y. Wang and J. M. Garibaldi, “Simulated annealing fuzzy clustering in cancer diagnosis,” Informatica, vol. 29, no. 1, 2005.

S. Sunarti, F. F. Rahman, M. Naufal, M. Risky, K. Febriyanto, and R. Masnina, “Artificial intelligence in healthcare: opportunities and risk for future,” Gac. Sanit., vol. 35, pp. S67--S70, 2021.

M. Masmoudi, B. Jarboui, and P. Siarry, “Artificial Intelligence and Data Mining in Healthcare.” Springer, 2020.

S. Shamshirband, M. Fathi, A. Dehzangi, A. T. Chronopoulos, and H. Alinejad-Rokny, “A Review on Deep Learning Approaches in Healthcare Systems: Taxonomies, Challenges, and Open Issues,” J. Biomed. Inform., p. 103627, 2020.

M. Casamayor, R. Morlock, H. Maeda, and J. Ajani, “Targeted literature review of the global burden of gastric cancer,” Ecancermedicalscience, vol. 12, 2018.

M. Akcay, D. Etiz, and O. Celik, “Prediction of Survival and Recurrence Patterns by Machine Learning in Gastric Cancer Cases Undergoing Radiation Therapy and Chemotherapy,” Adv. Radiat. Oncol., vol. 5, no. 6, pp. 1179–1187, 2020.

T. Saba, “Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges,” J. Infect. Public Health, vol. 13, no. 9, pp. 1274–1289, 2020.

S.-L. Zhu, J. Dong, C. Zhang, Y.-B. Huang, and W. Pan, “Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics,” PLoS One, vol. 15, no. 12, p. e0244869, 2020.

A. N. Richter and T. M. Khoshgoftaar, “A review of statistical and machine learning methods for modeling cancer risk using structured clinical data,” Artif. Intell. Med., vol. 90, pp. 1–14, 2018.

A. Yasar, I. Saritas, and H. Korkmaz, “Computer-aided diagnosis system for detection of stomach cancer with image processing techniques,” J. Med. Syst., vol. 43, no. 4, pp. 1–11, 2019.

J. Y. Park and R. Herrero, “Recent progress in gastric cancer prevention,” Best Pract. & Res. Clin. Gastroenterol., p. 101733, 2021.

A. Onasanya and M. Elshakankiri, “Smart integrated IoT healthcare system for cancer care,” Wirel. Networks, pp. 1–16, 2019.

M. D. Islam, W. A. Kaplan, D. Trachtenberg, R. Thrasher, K. P. Gallagher, and V. J. Wirtz, “Impacts of intellectual property provisions in trade treaties on access to medicine in low and middle income countries: a systematic review,” Global. Health, vol. 15, no. 1, p. 88, 2019.

K. J. Cios, B. Krawczyk, J. Cios, and K. J. Staley, “Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine,” arXiv Prepr. arXiv1905.09203, 2019.

M. Kumari and V. Singh, “Breast cancer prediction system,” Procedia Comput. Sci., vol. 132, pp. 371–376, 2018.

G. Purusothaman and P. Krishnakumari, “A survey of data mining techniques on risk prediction: Heart disease,” Indian J. Sci. Technol., vol. 8, no. 12, p. 1, 2015.

L. Goshayeshi et al., “Predictive model for survival in patients with gastric cancer,” Electron. physician, vol. 9, no. 12, p. 6035, 2017.

N. C. Caballé, J. L. Castillo-Sequera, J. A. Gómez-Pulido, and M. L. Polo-Luque, “Machine learning applied to diagnosis of human diseases: A systematic review,” 2020.

W. H. Organization and others, “Cancer. 2018,” World Heal. Organ. Available http//www. who. int/mediacentre/factsheets/fs297/en, 2017.

A. Mortezagholi, O. Khosravizadehorcid, M. B. Menhaj, Y. Shafigh, and R. Kalhor, “Make intelligent of gastric cancer diagnosis error in Qazvin’s medical centers: Using data mining method,” Asian Pacific J. Cancer Prev., vol. 20, no. 9, pp. 2607–2610, 2019, doi: 10.31557/APJCP.2019.20.9.2607.

A. Kalantari, A. Kamsin, S. Shamshirband, A. Gani, H. Alinejad-Rokny, and A. T. Chronopoulos, “Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions,” Neurocomputing, vol. 276, pp. 2–22, 2018.

A. Shrivastava and S. S. Tomar, “A hybrid framework for heart disease prediction: review and analysis,” Int. J. Adv. Technol. Eng. Explor., vol. 3, no. 15, p. 21, 2016.

W. Wu and H. Zhou, “Data-driven diagnosis of cervical cancer with support vector machine-based approaches,” IEEE Access, vol. 5, pp. 25189–25195, 2017.

J. S. Sartakhti, M. H. Zangooei, and K. Mozafari, “Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA),” Comput. Methods Programs Biomed., vol. 108, no. 2, pp. 570–579, 2012.

A. I. Pritom, M. A. R. Munshi, S. A. Sabab, and S. Shihab, “Predicting breast cancer recurrence using effective classification and feature selection technique,” in 2016 19th International Conference on Computer and Information Technology (ICCIT), 2016, pp. 310–314.

S. Turgut, M. Daugtekin, and T. Ensari, “Microarray breast cancer data classification using machine learning methods,” in 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), 2018, pp. 1–3.

R. Kannan and V. Vasanthi, “Machine learning algorithms with ROC curve for predicting and diagnosing the heart disease,” in Soft Computing and Medical Bioinformatics, Springer, 2019, pp. 63–72.

R. Chauhan, R. Jangade, and R. Rekapally, “Classification model for prediction of heart disease,” in Soft Computing: Theories and Applications, Springer, 2018, pp. 707–714.

K. Uyar and A. .Ilhan, “Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks,” Procedia Comput. Sci., vol. 120, pp. 588–593, 2017.

H. David and S. A. Belcy, “HEART DISEASE PREDICTION USING DATA MINING TECHNIQUES.,” ICTACT J. Soft Comput., vol. 9, no. 1, 2018.

N. Akhtar, M. R. Talib, and N. Kanwal, “Data Mining Techniques to Construct a Model: Cardiac Diseases,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 1, 2018.

A. Dey, J. Singh, and N. Singh, “Analysis of supervised machine learning algorithms for heart disease prediction with reduced number of attributes using principal component analysis,” Int. J. Comput. Appl., vol. 140, no. 2, pp. 27–31, 2016.

L. Parthiban and R. Subramanian, “Intelligent heart disease prediction system using CANFIS and genetic algorithm,” Int. J. Biol. Biomed. Med. Sci., vol. 3, no. 3, 2008.

I. A. Zriqat, A. M. Altamimi, and M. Azzeh, “A comparative study for predicting heart diseases using data mining classification methods,” arXiv Prepr. arXiv1704.02799, 2017.

M. Nishio et al., “Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization,” PLoS One, vol. 13, no. 4, p. e0195875, 2018.

Y. Zhao, Y. Liu, and W. Huang, “Prediction model of HBV reactivation in primary liver cancer's Based on NCA feature selection and SVM classifier with Bayesian and grid optimization,” in 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2018, pp. 547–551.

H. Almarabeh and E. Amer, “A study of data mining techniques accuracy for healthcare,” Int. J. Comput. Appl., vol. 168, no. 3, pp. 12–17, 2017.

S. A. Mahmoodi, K. Mirzaie, and S. M. Mahmoudi, “A new algorithm to extract hidden rules of gastric cancer data based on ontology,” Springerplus, vol. 5, no. 1, p. 312, 2016.

M.-M. Liu, L. Wen, Y.-J. Liu, Q. Cai, L.-T. Li, and Y.-M. Cai, “Application of data mining methods to improve screening for the risk of early gastric cancer,” BMC Med. Inform. Decis. Mak., vol. 18, no. 5, p. 121, 2018.

Y. Amirgaliyev, S. Shamiluulu, T. Merembayev, and D. Yedilkhan, “Using Machine Learning Algorithm for Diagnosis of Stomach Disorders,” in International Conference on Mathematical Optimization Theory and Operations Research, 2019, pp. 343–355.

A. Rajkomar et al., “Scalable and accurate deep learning with electronic health records,” NPJ Digit. Med., vol. 1, no. 1, pp. 1–10, 2018.

P. Yadav, M. Steinbach, V. Kumar, and G. Simon, “Mining electronic health records (EHRs) A survey,” ACM Comput. Surv., vol. 50, no. 6, pp. 1–40, 2018.

S. Shirazi, H. Baziyad, and H. Karimi, “An Application-Based Review of Recent Advances of Data Mining in Healthcare,” J. Biostat. Epidemiol., 2019.

V. V Petrov, O. P. Mintser, A. A. Kryuchyn, and Y. A. Kryuchyna, “Big Data in medicine: promise and challenges,” 2019.

D. Cirillo and A. Valencia, “Big data analytics for personalized medicine,” Curr. Opin. Biotechnol., vol. 58, pp. 161–167, 2019.

DOI: https://doi.org/10.31449/inf.v45i7.3633

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.