CLASSIFICATION OF TUMORS BASED ON GENETIC EXPRESSIONS

Marã A Elena Acevedo-Mosqueda, Sandra Dinora Orantes-Jimã‰nez, Marco Antonio Acevedo-Mosqueda and Ricardo Carreã‘o Aguilera
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Marã A Elena Acevedo-Mosqueda: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica Av. Luis Enrique Erro S/N, Unidad Profesional “Adolfo López Mateos†Edificio Z, Tercer Piso Zacatenco, Alcaldía Gustavo A. Madero, C.P. 07738, Ciudad de México, Mexico
Sandra Dinora Orantes-Jimã‰nez: ��Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Alcaldía Gustavo A. Madero C.P. 07738, Ciudad de México, Mexico
Marco Antonio Acevedo-Mosqueda: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica Av. Luis Enrique Erro S/N, Unidad Profesional “Adolfo López Mateos†Edificio Z, Tercer Piso Zacatenco, Alcaldía Gustavo A. Madero, C.P. 07738, Ciudad de México, Mexico
Ricardo Carreã‘o Aguilera: ��Universidad del Istmo, Campus Tehuantepec, Ciudad Universitaria S/N, Barrio Santa Cruz, 4a. Sección Sto. Domingo Tehuantepec, C.P. 70760, Oaxaca, Mexico

FRACTALS (fractals), 2022, vol. 30, issue 07, 1-13

Abstract: This paper analyzes the ability of different machine learning algorithms to find patterns in the levels of gene expression for the correct classification of the five different types of tumors: breast, colon, kidney, lung, and prostate. The machine learning techniques were selected according to the most used algorithms in the related works: Bayesian method, Decision Trees, and K-Nearest Neighbors. Three metrics were applied to test the performance of the classifiers: Precision, Recall, and F1-score. The results of Precision of the algorithms were 95.03% (Bayesian), 96.73% (Decision Trees), and 99.52% (K-Nearest Neighbors). A software prototype was developed to classify tumors based on genetic expressions utilizing these three algorithms with satisfactory results.

Keywords: Artificial Intelligence; Machine Learning; Classification; Genetic Expressions; Tumors (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:30:y:2022:i:07:n:s0218348x22501742

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DOI: 10.1142/S0218348X22501742

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