TY - JOUR
T1 - Classification Of Tumors Based on Genetic Expressions
AU - Acevedo-Mosqueda, María Elena
AU - Orantes-Jiménez, Sandra Dinora
AU - Acevedo-Mosqueda, Marco Antonio
AU - Aguilera, Ricardo Carreño
N1 - Publisher Copyright:
© 2022 The Author(s).
PY - 2022/11/1
Y1 - 2022/11/1
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Classification
KW - Genetic Expressions
KW - Machine Learning
KW - Tumors
UR - http://www.scopus.com/inward/record.url?scp=85141245131&partnerID=8YFLogxK
U2 - 10.1142/S0218348X22501742
DO - 10.1142/S0218348X22501742
M3 - Artículo
AN - SCOPUS:85141245131
SN - 0218-348X
VL - 30
JO - Fractals
JF - Fractals
IS - 7
M1 - 2250174
ER -