Volume 21 , Issue 1 , PP: 174-183, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Neyda Hernández Bandera 1 * , Jenny M. Moya Arizaga 2 , Enrique Rodríguez Reyes 3
Doi: https://doi.org/10.54216/IJNS.210116
CKD, or chronic kidney failure, is characterized by a gradual decline in kidney operation over time and may be linked to a wide range of medical conditions. Initial detection and therapy are the best tools for combating chronic kidney disease, although they often only delay the development of renal failure. The eGFR-based CKD grading system is useful for risk stratification, patient monitoring, and treatment strategy development. Personalized care and treatment planning will be possible if this research is successful in predicting how soon a CKD individual will need to begin dialysis. The machine learning methods used to predict CKD. But the dataset contains uncertain information, so the neutrosophic set is used to overcome this issue. This paper suggests a framework including the neutrosophic DEMATEL and machine learning method to predict CKD. The neutrosophic DEMATEL method is used to give weights to all variables of the dataset. Then conduct the preprocessing data to eliminate the variables with the least weight. The three machine learning methods used in this paper are Gradient Boosting (GB), Ada Boosting (AB), and Random Forest (RF). The results show the accuracy of the three algorithms. The AB has a 99.166% accuracy, and it is the highest accuracy in this paper followed by the GB has 98.3%, then RF has 92.85%.
Neutrosophic Set , DEMATEL , Machine Learning , Kidney , Prediction
[1] M. U. Emon, R. Islam, M. S. Keya, and R. Zannat, “Performance Analysis of Chronic Kidney Disease through Machine Learning Approaches,” in 2021 6th International Conference on Inventive Computation Technologies (ICICT), IEEE, 2021, pp. 713–719.
[2] F. Sanmarchi, C. Fanconi, D. Golinelli, D. Gori, T. Hernandez-Boussard, and A. Capodici, “Predict, diagnose, and treat chronic kidney disease with machine learning: A systematic literature review,” J. Nephrol., pp. 1–17, 2023.
[3] P. Ghosh, F. M. J. M. Shamrat, S. Shultana, S. Afrin, A. A. Anjum, and A. A. Khan, “Optimization of prediction method of chronic kidney disease using machine learning algorithm,” in 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), IEEE, 2020, pp. 1–6.
[4] J. Xiao et al., “Comparison and development of machine learning tools in the prediction of chronic kidney disease progression,” J. Transl. Med., vol. 17, no. 1, pp. 1–13, 2019.
[5] M. Almasoud and T. E. Ward, “Detection of chronic kidney disease using machine learning algorithms with least number of predictors,” Int. J. Soft Comput. Its Appl., vol. 10, no. 8, 2019.
[6] W. Wang, G. Chakraborty, and B. Chakraborty, “Predicting the risk of chronic kidney disease (ckd) using machine learning algorithm,” Appl. Sci., vol. 11, no. 1, p. 202, 2020.
[7] Mahmoud Ismail , Shereen Zaki, Neutrosophic MCDM Methodology to Select Best Industrial Arc Welding Robot, Neutrosophic and Information Fusion, Vol. 1 , No. 1 , (2023) : 08-16 (Doi : https://doi.org/10.54216/NIF.010101)
[8] Ahmed M. Ali, Ranking Renewable Energy Alternatives by using Triangular Neutrosophic Sets Integrated with MCDM, Neutrosophic and Information Fusion, Vol. 1 , No. 1 , (2023) : 17-26 (Doi : https://doi.org/10.54216/NIF.010102
[9] H. S. Kilic, P. Yurdaer, and C. Aglan, “A leanness assessment methodology based on neutrosophic DEMATEL,” J. Manuf. Syst., vol. 59, pp. 320–344, 2021.
[10] Ahmed Sleem , Ibrahim Elhenawy, An Interval Valued Neutrosophic Sets Integrated with the AHP MCDM Methodology to Assess the Station of 5G Network, Neutrosophic and Information Fusion, Vol. 1 , No. 1 , (2023) : 34-40 (Doi : https://doi.org/10.54216/NIF.010104)
[11] J. Qin, L. Chen, Y. Liu, C. Liu, C. Feng, and B. Chen, “A machine learning methodology for diagnosing chronic kidney disease,” IEEE Access, vol. 8, pp. 20991–21002, 2019.
[12] Y. Amirgaliyev, S. Shamiluulu, and A. Serek, “Analysis of chronic kidney disease dataset by applying machine learning methods,” in 2018 IEEE 12th International Conference on Application of Information and Communication Technologies (AICT), IEEE, 2018, pp. 1–4.
[13] E. Dritsas and M. Trigka, “Machine learning techniques for chronic kidney disease risk prediction,” Big Data Cogn. Comput., vol. 6, no. 3, p. 98, 2022.
[14] A. Charleonnan, T. Fufaung, T. Niyomwong, W. Chokchueypattanakit, S. Suwannawach, and N. Ninchawee, “Predictive analytics for chronic kidney disease using machine learning techniques,” in 2016 management and innovation technology international conference (MITicon), IEEE, 2016, p. MIT-80.
[15] K. R. A. Padmanaban and G. Parthiban, “Applying machine learning techniques for predicting the risk of chronic kidney disease,” Indian J. Sci. Technol., vol. 9, no. 29, pp. 1–6, 2016.
[16] Ahmed M. Ali , Ahmed Abdelmouty, Ranking Sustainable Technologies in Wave Energy: Multi-Criteria Decision-Making Approach under Neutrosophic Sets, American Journal of Business and Operations Research, Vol. 10 , No. 2 , (2023) : 14-22 (Doi : https://doi.org/10.54216/AJBOR.100202) [17] C. Veeramani, R. Venugopal, and S. A. Edalatpanah, “Neutrosophic DEMATEL approach for financial ratio performance evaluation of the NASDAQ Exchange,” Neutrosophic Sets Syst., vol. 51, pp. 766–782, 2022.
[18] Mahmoud Ismail , Mahmoud Ibrahiem, Multi-Criteria Decision-Making Approach based on Neutrosophic Sets for Evaluating Sustainable Supplier Selection in the Industrial 4.0, American Journal of Business and Operations Research, Vol. 7 , No. 2 , (2022) : 41-55 (Doi : https://doi.org/10.54216/AJBOR.070204)
[19] Abduallah Gamal , Nehal Nabil Mostafa, Sustainable Supplier Selection using Neutrosophic Sets and MCDM Framework, Neutrosophic and Information Fusion, Vol. 1 , No. 1 , (2023) : 27-33 (Doi : https://doi.org/10.54216/NIF.010103)
[20] Myvizhi M. , Samah I. Abdel Aal, A Multi-Criteria Decision-Making Model Based on Bipolar Neutrosophic Sets for the Selection of Battery Electric Vehicles, American Journal of Business and Operations Research, Vol. 10 , No. 2 , (2023) : 23-31 (Doi : https://doi.org/10.54216/AJBOR.100203)
[21] S. Revathy, B. Bharathi, P. Jeyanthi, and M. Ramesh, “Chronic kidney disease prediction using machine learning models,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 6364–6367, 2019.
[22] A. J. Aljaaf et al., “Early prediction of chronic kidney disease using machine learning supported by predictive analytics,” in 2018 IEEE congress on evolutionary computation (CEC), IEEE, 2018, pp. 1–9.
[23] S. Tekale, P. Shingavi, S. Wandhekar, and A. Chatorikar, “Prediction of chronic kidney disease using machine learning algorithm,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 7, no. 10, pp. 92–96, 2018.
[24] E. Dovgan et al., “Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients,” PLoS One, vol. 15, no. 6, p. e0233976, 2020.
[25] Hernández, Noel Batista, et al. "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.
[26] Ricardo, J. E., Fernández, A. J. R., Martínez, T. T. C., & Calle, W. A. C, "Analysis of Sustainable Development Indicators through Neutrosophic Correlation Coefficients." Neutrosophic Sets and Systems, vol 52 , 2022
