Classification Of Cancer Genome Atlas Glioblastoma Multiform (TCGA-GBM) Using Machine Learning Method

Abstract

Glioblastoma multiforme (GBM) is a highly ma-lignant type of brain cancer with a bleak prognosis. This study aimed to apply machine learning methods to classify GBM samples from the Cancer Genome Atlas (TCGA) dataset. Several supervised learning algorithms, including Support Vector Machine, Ad boost, Neural Network, and Decision Tree, were employed in the analysis. Our findings indicate that the Decision Tree algorithm was the most effective for this classification task, achieving an impressive 99% accuracy. Our study provides evidence that machine learning can accurately classify GBM samples in large-scale genomic datasets, enabling a deeper understanding of the genomic characteristics of this cancer. This study emphasizes the potential of machine learning approaches for improved cancer diagnosis and treatment through the analysis of large-scale genomic datasets.