Skip to main content
SpringerLink
Log in

Fuzzy Inference System Based-AI for Diagnosis of Esophageal Cancer

  • Chapter
  • First Online:
Machine Learning and the Internet of Things in Education

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1115))

  • 176 Accesses

Abstract

Due to global changes, the incidence and mortality rate of esophagus cancer has skyrocketed in the last decades, with about 500,000 new cases. Esophageal cancer is a real life problem with uncertain data and human error, giving room for possible misdiagnosis. This study developed a fuzzy intelligent system (FIS) to screen and provide predictive diagnosis of esophageal cancer. Fuzzy IF THEN rules were generated from a combination of esophageal symptoms, general risk factors, and diagnostic tests, under expert considerations. MATLAB software was used to design and run the FIS. The data was retrieved from a hospital in Erbil for 7 patients. The system provides recommendations with each predictive diagnosis, whether a patient is positive or negative for esophageal cancer or something suspicious is wrong with the esophagus. After implementing the data on FIS, the system shows an overall system accuracy of 95.24%, with an even higher accuracy of 98% for each patient’s prediction. For future studies, it is highly recommended that the fuzzy rules be expanded to include more variables and dataset.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. 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 

  2. Corona, E., Yang, L., Esrailian, E., et al. (2021). Trends in esophageal cancer mortality and stage at diagnosis by race and ethnicity in the United States. Cancer Causes and Control, 32, 883–894.

    Article  Google Scholar 

  3. Huang, F. L., & Yu, S. J. (2018). Esophageal cancer: Risk factors, genetic association, and treatment. Asian Journal of Surgery, 41, 210–215.

    Article  Google Scholar 

  4. Buckle, G. C., Mmbaga, E. J., Paciorek, A., Akoko, L., Deardorff, K., Mgisha, W., Mushi, B. P., Mwaiselage, J., Hiatt, R. A., Zhang, L., & Van Loon, K. (2022). Risk factors associated with early-onset esophageal cancer in Tanzania. JCO Global Oncology, 2022, 8.

    Google Scholar 

  5. Thakkar, S., & Kaul, V. (2020). Endoscopic ultrasound staging of esophageal cancer. Gastroenterology & Hepatology, 16(1).

    Google Scholar 

  6. Ferlay, J., Soerjomataram, I., Dikshit, R., et al. (2015). Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer, 136(5), E359–E386.

    Article  Google Scholar 

  7. Huang, J., Koulaouzidis, A., Marlicz, W., Lok, V., Chu, C., Ngai, C. H., Zhang, L., Chen, P., Wang, S., Yuan, J., et al. (2021). Global burden, risk factors, and trends of esophageal cancer: An analysis of cancer registries from 48 countries. Cancers, 13, 141.

    Article  Google Scholar 

  8. Salek, R., Safa, E. B., Hamid, S. S., et al. (2009). A geographic area with better outcome of esophageal carcinoma: Is there an effect of ethnicity and etiologic factors? Oncology, 77, 172–177.

    Article  Google Scholar 

  9. Arnold, M., Ferlay, J., van Berge Henegouwen, M. I., et al. (2020). Global burden of esophageal and gastric cancer by histology and subsite in 2018. Gut, 69, 1564–1571.

    Article  Google Scholar 

  10. Simba, H., Tromp, G., Sewram, V., Mathew, C. G., Chen, W. C., & Kuivaniemi, H. (2022). Esophageal cancer genomics in Africa: Recommendations for future research. Frontiers in Genetics, 13, 864575.

    Article  Google Scholar 

  11. Hopkins Medicine. (2022). Warning signs of esophageal cancer. Retrieved April 11, 2023. https://www.hopkinsmedicine.org/kimmel_cancer_center/cancers_we_treat/esophageal_cancer/warning-signs.html

  12. Chen, Y., Chen, X., Yu, H., Zhou, H., & Xu, S. (2019). Oral microbiota as promising diagnostic biomarkers for gastrointestinal cancer: A systematic review. OncoTargets and Therapy, 12, 11131–11144.

    Article  Google Scholar 

  13. Fraccaro, P., O’Sullivan, D., Plastiras, P., O’Sullivan, H., Dentone, C., Di Biago, A., & Weller, P. (2015). Behind the screens: Clinical decision support methodologies–a review. Health Policy and Technology, 4(1), 29–38.

    Article  Google Scholar 

  14. Wang, C., Lee, T., & Fang, C., et al. (2012). Fuzzy logic-based prognostic score for outcome prediction in esophageal cancer. IEEE Transactions on Information Technology in Biomedicine, 16(6).

    Google Scholar 

  15. Hamed, R. I. (2015). Esophageal cancer prediction based on qualitative features using adaptive fuzzy reasoning method. Journal of King Saud University–Computer and Information Sciences, 27, 129–139.

    Google Scholar 

  16. Scrobotă, I., Băciuț, G., & Filip, A. G., et al. (2017). Application of fuzzy logic in oral cancer risk assessment. Iranian Journal of Public Health, 46(5), 612–619.

    Google Scholar 

  17. Li, S., Zheng, L., & Zhang, Y., et al. (2018). Automatic segmentation of esophageal cancer pathological sections based on semantic segmentation. In 2018 International Conference on Orange Technologies (ICOT) (pp. 1–5). https://doi.org/10.1109/ICOT.2018.8705880

  18. Du, W., Rao, N., & Dong, C., et al. (2021). Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network. Biomedical Optics Express 3066, 12(6).

    Google Scholar 

  19. Fang, Y., Mukundan, A., & Tsao, Y., et al. (2022). Identification of early esophageal cancer by semantic segmentation. Journal of Personalized Medicine, 12(8), 1204.

    Google Scholar 

  20. Farokhzad, M. R., & Ebrahimi, I. (2016). A novel adaptive neuro fuzzy inference system for the diagnosis of liver disease. International journal of academic research in computer engineering, 1(1), 61–66.

    Google Scholar 

  21. Abiyev, R., Arslan, M., Bush Idoko, J., Sekeroglu, B., & Ilhan, A. (2020). Identification of epileptic EEG signals using convolutional neural networks. Applied sciences, 10(12), 4089.

    Article  Google Scholar 

  22. Abiyev, R. H., Arslan, M. & Idoko, J. B. (2020). Sign language translation using deep convolutional neural networks. KSII Transactions on Internet & Information Systems, 14(2).

    Google Scholar 

  23. Helwan, A., Idoko, J. B., & Abiyev, R. H. (2017). Machine learning techniques for classification of breast tissue. Procedia computer science, 120, 402–410.

    Article  Google Scholar 

  24. Sekeroglu, B., Abiyev, R., Ilhan, A., Arslan, M., & Idoko, J. B. (2021). Systematic literature review on machine learning and student performance prediction: Critical gaps and possible remedies. Applied Sciences, 11(22), 10907.

    Article  Google Scholar 

  25. Idoko, J. B., Arslan, M., & Abiyev, R. (2018). Fuzzy neural system application to differential diagnosis of erythemato-squamous diseases. Cyprus J Med Sci, 3(2), 90–97.

    Article  Google Scholar 

  26. Ma’aitah, M. K. S., Abiyev, R. & Bush, I.J. (2017). Intelligent classification of liver disorder using fuzzy neural system. International Journal of Advanced Computer Science and Applications, 8(12).

    Google Scholar 

  27. Bush, I.J., Abiyev, R., Ma’aitah, M.K.S., & Altıparmak, H. (2018). Integrated artificial intelligence algorithm for skin detection. In ITM Web of Conferences (Vol. 16, p. 02004). EDP Sciences.

    Google Scholar 

  28. Bush, I. J., Abiyev, R., & Arslan, M. (2019). Impact of machine learning techniques on hand gesture recognition. Journal of Intelligent & Fuzzy Systems, 37(3), 4241–4252.

    Article  Google Scholar 

  29. Uwanuakwa, I. D., Idoko, J. B., Mbadike, E., Reşatoğlu, R, Alaneme, G. (2022). Application of deep learning in structural health management of concrete structures. In Proceedings of the Institution of Civil Engineers-Bridge Engineering (pp. 1–8). Thomas Telford Ltd.

    Google Scholar 

  30. Helwan, A., Dilber, U. O., Abiyev, R., & Bush, J. (2017). One-year survival prediction of myocardial infarction. International Journal of Advanced Computer Science and Applications, 8(6). https://doi.org/10.14569/IJACSA.2017.080622

  31. Bush, I. J., Abiyev, R. H., & Mohammad, K. M. (2017). Intelligent machine learning algorithms for colour segmentation. WSEAS Transactions on Signal Processing, 13, 232–240.

    Google Scholar 

  32. Dimililer, K., & Bush, I.J. (2017). Automated classification of fruits: Pawpaw fruit as a case study. In Man-Machine Interactions 5: 5th International Conference on Man-Machine Interactions, ICMMI 2017 Held at Kraków, Poland, October 3–6, 2017 (pp. 365–374). Cham: Springer International Publishing.

    Google Scholar 

  33. Bush, I. J., & Dimililer, K. (2017). Static and dynamic pedestrian detection algorithm for visual based driver assistive system. In ITM Web of Conferences (Vol. 9, p. 03002). EDP Sciences.

    Google Scholar 

  34. Abiyev, R., Idoko, J. B., & Arslan, M. (2020). Reconstruction of convolutional neural network for sign language recognition. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (pp. 1–5). IEEE.

    Google Scholar 

  35. Abiyev, R., Idoko, J. B., Altıparmak, H., & Tüzünkan, M. (2023). Fetal health state detection using interval type-2 fuzzy neural networks. Diagnostics, 13(10), 1690.

    Article  Google Scholar 

  36. Arslan, M., Bush, I.J., Abiyev, R.H. (2019). Head movement mouse control using convolutional neural network for people with disabilities. In 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing—ICAFS-2018 (vol. 13, pp. 239–248). Springer International Publishing.

    Google Scholar 

  37. Abiyev, R.H., Idoko, J.B., & Dara, R. (2022). Fuzzy neural networks for detection kidney diseases. In Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, held August 24–26, 2021. Volume 2 (pp. 273–280). Springer International Publishing.

    Google Scholar 

  38. Uwanuakwa, I.D., Isienyi, U.G., Bush Idoko, J., & Ismael Albrka, S. (2020). Traffic warning system for wildlife road crossing accidents using artificial intelligence. In International Conference on Transportation and Development 2020 (pp. 194–203). Reston, VA: American Society of Civil Engineers.

    Google Scholar 

  39. Idoko, B., Idoko, J.B., Kazaure, Y.Z.M., Ibrahim, Y.M., Akinsola, F. A., Raji, A.R. (2022). IoT based motion detector using raspberry Pi gadgetry. In 2022 5th Information Technology for Education and Development (ITED) (pp. 1–5). IEEE.

    Google Scholar 

  40. Idoko, J.B., Arslan, M., & Abiyev, R.H. (2019). Intensive investigation in differential diagnosis of erythemato-squamous diseases. In Proc. 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing (ICAFS-2018) (Vol. 10, pp. 978–3).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Applied Artificial Intelligence Research Centre, Department of Computer Engineering, Near East University, Nicosia, 99138, Turkey

    John Bush Idoko

  2. Department of Computer Engineering, Near East University, Nicosia, 99138, Turkey

    Mohammed Jameel Sadeq

Authors
  1. John Bush Idoko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed Jameel Sadeq
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John Bush Idoko .

Editor information

Editors and Affiliations

  1. Computer Engineering Department, Near East University, Lefkosa, Cyprus

    John Bush Idoko

  2. Department of Computer Engineering, Near East University, Nicosia, Cyprus

    Rahib Abiyev

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Idoko, J.B., Sadeq, M.J. (2023). Fuzzy Inference System Based-AI for Diagnosis of Esophageal Cancer. In: Idoko, J.B., Abiyev, R. (eds) Machine Learning and the Internet of Things in Education. Studies in Computational Intelligence, vol 1115. Springer, Cham. https://doi.org/10.1007/978-3-031-42924-8_4

Download citation

Publish with us

Policies and ethics

Search

Navigation