CLASSIFICATION OF TUMORS BASED ON GENETIC EXPRESSIONS-Reference-Cited by-同舟云学术

CLASSIFICATION OF TUMORS BASED ON GENETIC EXPRESSIONS

Published:2022-10-19 Issue:07 Volume:30 Page:
ISSN:0218-348X
Container-title:Fractals
language:en
Short-container-title:Fractals

Author:

ACEVEDO-MOSQUEDA MARÍA ELENA¹,ORANTES-JIMÉNEZ SANDRA DINORA²,ACEVEDO-MOSQUEDA MARCO ANTONIO¹,AGUILERA RICARDO CARREÑO³

Affiliation:

1. 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

2. Instituto Politécnico Nacional，Centro de Investigación en Computación, 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

3. Universidad del Istmo, Campus Tehuantepec, Ciudad Universitaria S/N, Barrio Santa Cruz, 4a. Sección Sto. Domingo Tehuantepec, C.P. 70760, Oaxaca, Mexico

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 [Formula: see text]-Nearest Neighbors. Three metrics were applied to test the performance of the classifiers: Precision, Recall, and [Formula: see text]-score. The results of Precision of the algorithms were 95.03% (Bayesian), 96.73% (Decision Trees), and 99.52% ([Formula: see text]-Nearest Neighbors). A software prototype was developed to classify tumors based on genetic expressions utilizing these three algorithms with satisfactory results.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Applied Mathematics,Geometry and Topology,Modeling and Simulation

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218348X22501742

Reference31 articles.

1. Selecting critical features for data classification based on machine learning methods

2. An analysis on prediction of breast cancer using radius Nearest Neighbor algorithm over other classification algorithms

3. Gene expression cancer classification using modified K-Nearest Neighbors technique

4. An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: Classification and regression tree (CART) analysis

5. A tree-based multiclassification of breast tumor histopathology images through deep learning