CLASSIFICATION OF TUMORS BASED ON GENETIC EXPRESSIONS

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 F-1-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.

Automated summary

This paper analyzes the ability of different machine learning algorithms to find patterns in gene expression levels for the accurate classification of five different types of tumors. The results demonstrate that Bayesian method, Decision Trees, and K-Nearest Neighbors perform well in terms of classifier performance.