Robust Logistic Regression-based Diagnosis Method of Prostate Cancer Using Optimized Feature Selection on Race Specific Gene-expression Datasets.

Background/Aim: Prostate cancer (PCa) incidence varies significantly by race, with Black men experiencing nearly 1.8 times higher prevalence than White men in the USA. Current prostate specific antigen (PSA)-based diagnostics lack specificity, and many machine learning models fail to consider racial differences in gene expression. This study proposes a race-aware PCa detection framework using optimized feature selection to improve diagnostic accuracy and fairness. Materials and Methods: RNAseq-Count-STAR and clinical phenotype data from TCGA (554 patients) were analyzed. A feature selection pipeline integrating Differential Gene Expression analysis, Receiving Operating Characteristic (ROC) analysis, and Gene-Set Enrichment Analysis identified a 9-gene subset strongly associated with the PCa clinical pathway. The model was trained on White population data and validated on the Black population dataset using various data balancing techniques. Results: The 9-gene logistic regression model achieved 95% accuracy in the White population and 96.8% accuracy in the Black population. Fairness analysis indicated minimal disparity between groups (4% difference in demographic parity, Conclusion: This study introduces a race-specific PCa detection framework that improves diagnostic accuracy using targeted biomarkers. It addresses misclassification risks in race-agnostic models and emphasizes the need for race-aware gene expression in ML diagnostics. Beyond detection, it enables personalized treatment, advancing precision medicine in PCa care.