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International Journal of Neutrosophic Science
Volume 21 , Issue 2, PP: 118-128 , 2023 | Cite this article as | XML | Html |PDF

Title

Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study

  Juan Viteri Rodríguez 1 * ,   Julio Rea Martínez 2 ,   Freddy F. Jumbo Salazar 3

1  Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Ambato), Ecuador
    (ua.juanviteri@uniandes.edu.ec)

2  Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Santo Domingo), Ecuador
    ( ua.juliorm92@uniandes.edu.ec)

3  Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Ambato), Ecuador
    (ua.freddyjumbo@uniandes.edu.ec)


Doi   :   https://doi.org/10.54216/IJNS.210211

Received: February 19, 2023 Revised: May 01, 2023 Accepted: June 06, 2023

Abstract :

The third most common disease worldwide, colorectal cancer (CRC) is responsible for around 10% of annual cancer diagnoses. The success of personalized treatment hinges on the ability to recognize biomarkers linked with CRC longevity and forecast the prognosis of CRC patients. The goal of this research is to provide a novel approach to doing multi-attribute colorectal cancer analysis by using machine learning algorithms with multi-criteria decision-making (MCDM) methods and neutrosophic set (NS). The NS is used to overcome the uncertainty in the dataset. This paper used the neutrosophic AHP method to get the weights of features in the used dataset. Then the machine learning algorithms are used to give analysis and prediction of colorectal cancer. The decision tree (DT) and support vector machine (SVM) is used to analyze and predict colorectal cancer. The dataset has nine features like age, gender, dukes stage, location, and Disease-free survival. This paper shows the analysis of the dataset and the correlation among the features.

Keywords :

Machine Learning; AHP; MCDM; Neutrosophic Set; Colorectal Cancer.

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Cite this Article as :
Style #
MLA Juan Viteri Rodríguez, Julio Rea Martínez, Freddy F. Jumbo Salazar. "Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study." International Journal of Neutrosophic Science, Vol. 21, No. 2, 2023 ,PP. 118-128 (Doi   :  https://doi.org/10.54216/IJNS.210211)
APA Juan Viteri Rodríguez, Julio Rea Martínez, Freddy F. Jumbo Salazar. (2023). Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study. Journal of International Journal of Neutrosophic Science, 21 ( 2 ), 118-128 (Doi   :  https://doi.org/10.54216/IJNS.210211)
Chicago Juan Viteri Rodríguez, Julio Rea Martínez, Freddy F. Jumbo Salazar. "Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study." Journal of International Journal of Neutrosophic Science, 21 no. 2 (2023): 118-128 (Doi   :  https://doi.org/10.54216/IJNS.210211)
Harvard Juan Viteri Rodríguez, Julio Rea Martínez, Freddy F. Jumbo Salazar. (2023). Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study. Journal of International Journal of Neutrosophic Science, 21 ( 2 ), 118-128 (Doi   :  https://doi.org/10.54216/IJNS.210211)
Vancouver Juan Viteri Rodríguez, Julio Rea Martínez, Freddy F. Jumbo Salazar. Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study. Journal of International Journal of Neutrosophic Science, (2023); 21 ( 2 ): 118-128 (Doi   :  https://doi.org/10.54216/IJNS.210211)
IEEE Juan Viteri Rodríguez, Julio Rea Martínez, Freddy F. Jumbo Salazar, Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study, Journal of International Journal of Neutrosophic Science, Vol. 21 , No. 2 , (2023) : 118-128 (Doi   :  https://doi.org/10.54216/IJNS.210211)