1
Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Santo Domingo), Ecuador
(us.ximenatr20@uniandes.edu.ec)
2
Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Ambato), Ecuador
(ua.alvaromoina@uniandes.edu.ec)
3
Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Ambato), Ecuador
(ua.danielacobo@uniandes.edu.ec)
Abstract :
Celiac disease is an autoimmune illness that causes damage to the small intestine and, in some cases, the bones as well. Histological analysis of duodenal biopsies obtained during upper digestive endoscopy is required for a diagnosis. The production of antibodies may be detected by immunological testing by taking a blood sample. Histology takes a long time, and endoscopy is intrusive. This paper used the MCDM method to compute the objective the celiac disease. In statistical distribution theory, entropy is often employed as a proxy for the uncertainty, unpredictability, or chaos of experimental results. The literature's entropy approaches provide a numeric measure of a random variable's information but struggle to handle data with interval values. The results of an experiment with an unknown outcome are often presented in interval form. The entropy method is used to compute the weights of the criteria. The neutrosophic sets were used to overcome the uncertain information in this study. This paper used six criteria and nine alternatives. The results are shown in this study.
Keywords :
Neutrosophic Set; MCDM; Celica Disease; Entropy Method
References :
[1] C.-A. Stoleru, E. H. Dulf, and L. Ciobanu, “Automated detection of celiac disease using Machine Learning Algorithms,” Sci. Rep., vol. 12, no. 1, p. 4071, 2022.
[2] F. Piccialli et al., “Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease,” Sci. Rep., vol. 11, no. 1, p. 5683, 2021.
[3] J. E. W. Koh et al., “Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach,” Comput. Methods Programs Biomed., vol. 203, p. 106010, 2021.
[4] F. L. Caetano dos Santos, I. M. Michalek, K. Laurila, K. Kaukinen, J. Hyttinen, and K. Lindfors, “Automatic classification of IgA endomysial antibody test for celiac disease: a new method deploying machine learning,” Sci. Rep., vol. 9, no. 1, p. 9217, 2019.
[5] A. Mehandiratta, N. Vij, A. Khanna, P. Gupta, D. Gupta, and A. K. Gupta, “Prediction of celiac disease using machine-learning techniques,” in International Conference on Innovative Computing and Communications: Proceedings of ICICC 2019, Volume 1, Springer, 2020, pp. 663–673.
[6] E. Gnodi, R. Meneveri, and D. Barisani, “Celiac disease: From genetics to epigenetics,” World J. Gastroenterol., vol. 28, no. 4, p. 449, 2022.
[7] M. K. Sana, Z. M. Hussain, P. A. Shah, and M. H. Maqsood, “Artificial intelligence in celiac disease,” Comput. Biol. Med., vol. 125, p. 103996, 2020.
[8] J. Carreras, “Artificial intelligence analysis of celiac disease using an autoimmune discovery transcriptomic panel highlighted pathogenic genes including BTLA,” in Healthcare, MDPI, 2022, p. 1550.
[9] X. Wang et al., “Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction,” Comput. Methods Programs Biomed., vol. 187, p. 105236, 2020.
[10] M. Saken, M. B. YAĞCI, and N. YUMUŞAK, “Impact of image segmentation techniques on celiac disease classification usingscale invariant texture descriptors for standard flexible endoscopic systems,” Turkish J. Electr. Eng. Comput. Sci., vol. 29, no. 2, pp. 598–615, 2021.
[11] S. Sharma and S. Singh, “A Complementary Dual of Single-Valued Neutrosophic Entropy with Application to MAGDM,” Mathematics, vol. 10, no. 20, p. 3726, 2022.
[12] A. Kumar, C. P. Gandhi, Y. Zhou, H. Tang, and J. Xiang, “Fault diagnosis of rolling element bearing based on symmetric cross entropy of neutrosophic sets,” Measurement, vol. 152, p. 107318, 2020.
[13] P. Singh, “A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson’s disease,” Artif. Intell. Med., vol. 104, p. 101838, 2020.
[14] P. Singh, “A type-2 neutrosophic-entropy-fusion based multiple thresholding method for the brain tumor tissue structures segmentation,” Appl. Soft Comput., vol. 103, p. 107119, 2021.
[15] Nada A. Nabeeh , Alshaimaa A. Tantawy, A Neutrosophic Proposed Model for Evaluation Blockchain Technology in Secure Enterprise Distributed Applications, Journal of Cybersecurity and Information Management, Vol. 11 , No. 1 , (2023) : 08-21 (Doi : https://doi.org/10.54216/JCIM.110101)
[16] R. M. Pattanayak, H. S. Behera, and S. Panigrahi, “A non-probabilistic neutrosophic entropy-based method for high-order fuzzy time-series forecasting,” Arab. J. Sci. Eng., vol. 47, no. 2, pp. 1399–1421, 2022.
[17] Mehmet Merkepci , Mohammad Abobala, Security Model for Encrypting Uncertain Rational Data Units Based on Refined Neutrosophic Integers Fusion and El Gamal Algorithm, Fusion: Practice and Applications, Vol. 10 , No. 2 , (2023) : 35-41 (Doi : https://doi.org/10.54216/FPA.100203) [18] R. A. K. Sherwani, T. Arshad, M. Albassam, M. Aslam, and S. Abbas, “Neutrosophic entropy measures for the Weibull distribution: theory and applications,” Complex Intell. Syst., vol. 7, no. 6, pp. 3067–3076, 2021.
[19] Shimaa Said , Mahmoud M. Ibrahim , Mahmoud M. Ismail, An Integrated Multi-Criteria Decision-Making Approach for Identification and Ranking Solar Drying Barriers under Single-Valued Triangular Neutrosophic Sets (SVTNSs), Neutrosophic and Information Fusion, Vol. 2 , No. 1 , (2023) : 35-49 (Doi : https://doi.org/10.54216/NIF.020103)
[20] R. A. K. Sherwani et al., Neutrosophic Entropy Measures For The Normal Distribution: Theory And Applications. Infinite Study, 2021.
[21] Nada A. Nabeeh , Alshaimaa A. Tantawy, A Neutrosophic Model for Blockchain Platform Selection based on SWARA and WSM, Neutrosophic and Information Fusion, Vol. 1 , No. 2 , (2023) : 29-43 (Doi : https://doi.org/10.54216/NIF.010204)
[22] R. Tan, W. Zhang, and S. Chen, “Decision-making method based on grey relation analysis and trapezoidal fuzzy neutrosophic numbers under double incomplete information and its application in typhoon disaster assessment,” Ieee Access, vol. 8, pp. 3606–3628, 2019.
[23] Abdullah Ali Salamai, Evaluation and Selection of Cloud Service: A neutrosophic model, Neutrosophic and Information Fusion, Vol. 1 , No. 2 , (2023) : 16-25 (Doi : https://doi.org/10.54216/NIF.010202)
[24] A. Kumar, C. P. Gandhi, Y. Zhou, R. Kumar, and J. Xiang, “Variational mode decomposition based symmetric single valued neutrosophic cross entropy measure for the identification of bearing defects in a centrifugal pump,” Appl. Acoust., vol. 165, p. 107294, 2020.
[25] Ahmed Abdelhafeez , Hoda K Mohamed, Enhance the Performance of Bus Rapid Transit (BRT) through the Evaluation of Alternatives under an Integrated MCDM Neutrosophic Environment, Neutrosophic and Information Fusion, Vol. 1 , No. 2 , (2023) : 08-15 (Doi : https://doi.org/10.54216/NIF.010201)
[26] Mai Mohamed, Modified Approach for Optimization of Real-Life Transportation Problem in Neutrosophic Environment: Suggested Modifications, American Journal of Business and Operations Research, Vol. 4 , No. 2 , (2021) : 65-76 (Doi : https://doi.org/10.54216/AJBOR.040204)
[27] Hernández, N. B., Vázquez, M. Y. L., Caballero, E. G., Cruzaty, L. E. V., Chávez, W. O., & Smarandache, "A new method to assess entrepreneurship competence in university students using based on plithogenic numbers and SWOT analysis." International Journal of Fuzzy Logic and Intelligent Systems, vol 21, pp.280-292, 2021.
[28] Ricardo, J. E., A. J. R. Fernández, M. Y. L. Vázquez. "Compensatory Fuzzy Logic with Single Valued Neutrosophic Numbers in the Analysis of University Strategic Management." International Journal of Neutrosophic Science, Vol. 18, pp. 151-159, 2022.
| Style | # |
|---|---|
| MLA | Ximena Trujillo Romero, Alvaro P. Moina Veloz, Daniela A. Cobo Álvarez. "Dietary Strategies for People with Celiac Disease by Neutrosophic Entropy Approach." International Journal of Neutrosophic Science, Vol. 21, No. 1, 2023 ,PP. 192-199 (Doi : https://doi.org/10.54216/IJNS.210118) |
| APA | Ximena Trujillo Romero, Alvaro P. Moina Veloz, Daniela A. Cobo Álvarez. (2023). Dietary Strategies for People with Celiac Disease by Neutrosophic Entropy Approach. Journal of International Journal of Neutrosophic Science, 21 ( 1 ), 192-199 (Doi : https://doi.org/10.54216/IJNS.210118) |
| Chicago | Ximena Trujillo Romero, Alvaro P. Moina Veloz, Daniela A. Cobo Álvarez. "Dietary Strategies for People with Celiac Disease by Neutrosophic Entropy Approach." Journal of International Journal of Neutrosophic Science, 21 no. 1 (2023): 192-199 (Doi : https://doi.org/10.54216/IJNS.210118) |
| Harvard | Ximena Trujillo Romero, Alvaro P. Moina Veloz, Daniela A. Cobo Álvarez. (2023). Dietary Strategies for People with Celiac Disease by Neutrosophic Entropy Approach. Journal of International Journal of Neutrosophic Science, 21 ( 1 ), 192-199 (Doi : https://doi.org/10.54216/IJNS.210118) |
| Vancouver | Ximena Trujillo Romero, Alvaro P. Moina Veloz, Daniela A. Cobo Álvarez. Dietary Strategies for People with Celiac Disease by Neutrosophic Entropy Approach. Journal of International Journal of Neutrosophic Science, (2023); 21 ( 1 ): 192-199 (Doi : https://doi.org/10.54216/IJNS.210118) |
| IEEE | Ximena Trujillo Romero, Alvaro P. Moina Veloz, Daniela A. Cobo Álvarez, Dietary Strategies for People with Celiac Disease by Neutrosophic Entropy Approach, Journal of International Journal of Neutrosophic Science, Vol. 21 , No. 1 , (2023) : 192-199 (Doi : https://doi.org/10.54216/IJNS.210118) |