Investigating Associations Between Prognostic Factors in Gliomas: Unsupervised Multiple Correspondence Analysis Background: Multiple Correspondence Analysis (MCA) is an unsupervised data science methodology that aims to identify and represent associations between categorical variables. Gliomas are an aggressive type of cancer characterized by diverse molecular and…

Investigating Associations Between Prognostic Factors in Gliomas: Unsupervised Multiple Correspondence Analysis #Gliomas #CancerResearch #PrognosticFactors #DataScience #MultipleCorrespondenceAnalysis