Accepted for/Published in: JMIR Bioinformatics and Biotechnology
Date Submitted: Mar 4, 2025
Date Accepted: Jun 20, 2025
Date Submitted to PubMed: Jun 25, 2025
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 to avoid overfitting caused by curse of dimensionality.
Objective:
This research aims to create a classification method to diagnose prostate cancer using gene expression in specific populations.
Methods:
This research uses Differential Gene Expression (DGE) 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:
As a result, 5 scenarios were created based on the number of feature selection genes, which consists of 4,7,9,13 and 139 features. The best performing model used 139 genes as features without oversampling achieved testing accuracies of 98% for the white race and 97% for the African American race.
Conclusions:
This research develops a race-specific diagnosis method for prostate cancer detection using enhanced feature selection and machine learning. The results emphasize the potential for developing unbiased diagnostic tools across diverse populations.
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Copyright
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