International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Prediction of the Type of Delivery using Fuzzy Inference System

  • Ayman M. Mansour (Department of Computer and Communication Engineering, Tafila Technical University)
  • Received : 2023.05.05
  • Published : 2023.05.30
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Abstract

In this paper a new fuzzy prediction is designed and developed to predict the type of delivery based on 7 factors. The developed system is highly needed to give a recommendation to the family excepting baby and at the same time provide an advisory system to the physician. The system has been developed using MATLAB and has been tested and verified using real data. The system shows high accuracy 95%. The results has been also checked one by one by a physician. The system shows perfect matching with the decision of the physician.

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References

  1. L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338-353,1965 https://doi.org/10.1016/S0019-9958(65)90241-X
  2. S. Begicheva, "Fuzzy Model for Evaluating the Quality of Medical Care," 2019 IEEE 21st Conference on Business Informatics (CBI), Moscow, Russia, 2019, pp. 5-8.
  3. D. Singh, S. Verma and J. Singla, "A Comprehensive Review of Intelligent Medical Diagnostic Systems," 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), Tirunelveli, India, 2020, pp. 977-981.
  4. A V. Kroshilin, A. N. Pylkin, S. V. Kroshilina and G. V. Ovechkin, "Managerial medical decisions and methods of obtaining medical information in conditions of uncertainty," 2021 10th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 2021, pp. 1-4.
  5. D. Singh, S. Verma and J. Singla, "A Neuro-fuzzy based Medical Intelligent System for the Diagnosis of Hepatitis B," 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, 2021, pp. 107-111.
  6. H. F. El-Sofany and I. A. T. F. Taj-Eddin, "A Cloud-based Model for Medical Diagnosis using Fuzzy Logic Concepts," 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, Egypt, 2019, pp. 162-167.
  7. Tamalika Chaira, "Application of Fuzzy/Intuitionistic Fuzzy Set in Image Processing," in Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set , Wiley, 2019, pp.237-257, doi: 10.1002/9781119544203.ch9.
  8. A Nasir, N. Jan, A. Gumaei and S. U. Khan, "Medical Diagnosis and Life Span of Sufferer Using Interval Valued Complex Fuzzy Relations," in IEEE Access, vol. 9, pp. 93764-93780, 2021. https://doi.org/10.1109/ACCESS.2021.3078185
  9. Y. Mori, H. Seki and M. Inuiguchi, "Knowledge Acquisition with Deep Fuzzy Inference Model and Its Application to a Medical Diagnosis," 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), Morioka, Japan, 2019, pp. 1-6.
  10. F. Lilik, S. Nagy, M. Kovacs, S. K. Szujo and L. T. Koczy, "Interpolative decisions in the fuzzy signature based image classification for liver CT," 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, Luxembourg, 2021, pp. 1-6.
  11. Uvaliyeva, M. Kalimoldayev, S. Rustamov and S. Belginova, "Fuzzy Logic for Medical Diagnosis of Clinical and Hematological Symptoms," 2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan, 2019, pp. 1-6.
  12. L. T. Hong Lan et al., "A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making," in IEEE Access, vol. 8, pp. 164899-164921, 2020. https://doi.org/10.1109/ACCESS.2020.3021097
  13. G. Selvachandran et al., "A New Design of Mamdani Complex Fuzzy Inference System for Multiattribute Decision Making Problems," in IEEE Transactions on Fuzzy Systems, vol. 29, no. 4, pp. 716-730, April 2021. https://doi.org/10.1109/TFUZZ.2019.2961350
  14. C. L. P. Chen, J. Wang, C. -H. Wang and L. Chen, "A New Learning Algorithm for a Fully Connected Neuro-Fuzzy Inference System," in IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 10, pp. 1741-1757, Oct. 2014. https://doi.org/10.1109/TNNLS.2014.2306915
  15. S. Kamthan and H. Singh, "Hierarchical Fuzzy Logic for Multi-Input Multi-Output Systems," in IEEE Access, vol. 8, pp. 206966-206981, 2020. https://doi.org/10.1109/ACCESS.2020.3037901
  16. F. Xiao, "A Hybrid Fuzzy Soft Sets Decision Making Method in Medical Diagnosis," in IEEE Access, vol. 6, pp. 25300-25312, 2018.
  17. Ayman M. Mansour, Mohammad A. Obaidat, Bilal Hawashin, " Elderly People Health Monitoring System using Fuzzy Rule Based Approach," International Journal of Advanced Computer Research (IJACR), vol. 4, no.17, pp. 904-914, December 2014.
  18. Landis, J.R.; Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics. 33 (1): 159-174. https://doi.org/10.2307/2529310