A Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach.

ABSTRACT:

Background

Previous machine learning approaches for prostate cancer detection using gene expression data have shown remarkable classification accuracies. However, prior studies overlook the influence of racial diversity within the population and the importance of selecting outlier genes based on expression profiles.

Objective

To develop a classification method for diagnosing prostate cancer using gene expression in specific populations.

Methods

This research uses Differentially Expressed Gene (DEG) analysis, Receiver Operating Characteristic (ROC) analysis, and MSigDB verification as a feature selection framework to identify genes for constructing Support Vector Machine (SVM) models.

Results

Among the models evaluated, the highest observed accuracy was achieved

SUBMITTER: Agustriawan D 

PROVIDER: S-EPMC12314727 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature