Logo PTI Logo rice

Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

Chronic kidney disease diagnosis using Fuzzy Knowledge Graph Pairs-based inference in the extreme case

, , , , , ,

DOI: http://dx.doi.org/10.15439/2022R35

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 8388 ()

Full text

Abstract. Chronic kidney disease is one of the diseases with high morbidity and mortality, commonly occurring in the general adult population, especially in people with diabetes and hypertension. Scientists have researched and developed intelligent medical systems to diagnose chronic kidney disease. Nevertheless, healthcare services remain low in resource-limited areas, and general practitioners are very short of clinical experience. Identifying chronic kidney disease in clinical practice remains challenging, especially for the general practitioner. This study proposes a model to develop a model for improving the efficiency of differential diagnosis. This paper presents a model consisting of a fuzzy knowledge graph pairs-based inference mechanism by accumulating the new rules to enrich the fuzzy rule base. A real-world dataset is gathered in Dien Bien hospital to evaluate the performance of our proposed model.

References

  1. Kovesdy, C. P. (2022). Epidemiology of chronic kidney disease: an update 2022. Kidney International Supplements, 12(1), 7-11.
  2. Ma, F., Sun, T., Liu, L., & Jing, H. (2020). Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network. Future Generation Computer Systems, 111, 17-26.
  3. Khade, A. A., Vidhate, A. V., & Vidhate, D. (2021, October). A comparative analysis of applied AI techniques for an early prediction of chronic kidney disease. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1386-1392). IEEE.
  4. Hosseinzadeh, M., Koohpayehzadeh, J., Bali, A. O., Asghari, P., Souri, A., Mazaherinezhad, A., ... & Rawassizadeh, R. (2021). A diagnostic prediction model for chronic kidney disease in internet of things platform. Multimedia Tools and Applications, 80(11), 16933-16950.
  5. Sharma, P. K., Sachdeva, A., & Bhargava, C. (2021). Fuzzy logic: A tool to predict the Renal diseases. Age, 1, 100.
  6. Lin, H. C., Hung, P. H., Hsieh, Y. Y., Lai, T. J., Hsu, H. T., Chung, M. C., & Chung, C. J. (2022). Long-term exposure to air pollutants and increased risk of chronic kidney disease in a community-based population using a fuzzy logic inference model. Clinical Kidney Journal.
  7. Ahmed, T. I., Bhola, J., Shabaz, M., Singla, J., Rakhra, M., More, S., & Samori, I. A. (2022). Fuzzy logic-based systems for the diagnosis of chronic kidney disease. BioMed Research International, 2022.
  8. Damodara, K., & Thakur, A. (2021, March). Adaptive neuro fuzzy inference system based prediction of chronic kidney disease. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 973-976). IEEE.
  9. Abiyev, R. H., Idoko, J. B., & Dara, R. (2021, August). Fuzzy Neural Networks for Detection Kidney Diseases. In International Conference on Intelligent and Fuzzy Systems (pp. 273-280). Springer, Cham.
  10. Long, C. K., Trung, H. Q., Thang, T. N., Dong, N. T., & Van Hai, P. (2021). A knowledge graph approach for the detection of digital human profiles in big data. Journal of Science and Technology: Issue on Information and Communications Technology, 19(6.2), 6-15.
  11. Lan, L. T. H., Tuan, T. M., Ngan, T. T., Giang, N. L., Ngoc, V. T. N., & Van Hai, P. (2020). A new complex fuzzy inference system with fuzzy knowledge graph and extensions in decision making. Ieee Access, 8, 164899-164921.
  12. Selvachandran, G., Quek, S. G., Lan, L. T. H., Giang, N. L., Ding, W., Abdel-Basset, M., & De Albuquerque, V. H. C. (2019). A new design of Mamdani complex fuzzy inference system for multi-attribute decision-making problems. IEEE Transactions on Fuzzy Systems, 29(4), 716-730.
  13. Tuan, T. M., Lan, L. T. H., Chou, S. Y., Ngan, T. T., Son, L. H., Giang, N. L., & Ali, M. (2020). M-CFIS-R: Mamdani complex fuzzy inference system with rule reduction using complex fuzzy measures in granular computing. Mathematics, 8(5), 707.
  14. Long, C. K., Van Hai, P., Tuan, T. M., Lan, L. T. H., Chuan, P. M., & Son, L. H. (2022). A novel fuzzy knowledge graph pairs approach in decision making. Multimedia Tools and Applications, 1-30.
  15. Long Cu Kim and Hai Pham Van (2018), “Intelligent Collaborative Decision Model for Simulation of Disaster Data in Cities and Urbanlization”, International Journal of Advanced Research (IJAR), Vol. 6, Issue 07.
  16. C. K. Long et al., (2020), “A Big Data Framework for eGovernment in Industry 4.0”, Open Computer Science, ISSN: 2299-1093.
  17. PHAM, Hai Van; TIEN, Dong Nguyen. Hybrid Louvain-Clustering Model Using Knowledge Graph for Improvement of Clustering User’s Behavior on Social Networks. In: The International Conference on Intelligent Systems & Networks. Springer, Singa-pore, 2021. p. 126-133.
  18. DINH, Xuan Truong; PHAM, Hai Van. Social Network Analysis Based on Combining Probabilistic Models with Graph Deep Learning. In: Communication and Intelligent Systems. Springer, Singapore, 2021. p. 975-986.
  19. Pham, H.V.; Thanh, D.H.; Moore, P. Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets. Sensors 2021, 21, 6070. https://doi.org/10.3390/s21186070.
  20. Hai Van Pham, Long Kim Cu, (2020), “Intelligent Rule-based Support Model Using Log Files in Big Data for Optimized Service Call Center Schedule”, Proceedings of International Conference on Research in Intelligent Computing in Engineering, ISBN 978-981-15-2780-7.
  21. C.K.Long et al. (2021), “Disease Diagnosis in the Traditional Medicine: A Novel Approach based on FKG-Pairs”, Journal of Research and Development on Information and Communication Technology, Vol. 2021(2), pp. 59-68.