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

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 We aim to develop a classification method for diagnosing prostate cancer using gene expression in specific populations. Methods This research uses differentially expressed gene analysis, receiver operating characteristic analysis, and MSigDB (Molecular Signature Database) verification as a feature selection framework to identify genes for constructing support vector machine models. Results Among the models evaluated, the highest observed accuracy was achieved using 139 gene features without oversampling, resulting in 98% accuracy for White patients and 97% for African American patients, based on 388 training samples and 92 testing samples. Notably, another model achieved a similarly strong performance, with 97% accuracy for White patients and 95% for African American patients, using only 9 gene features. It was trained on 374 samples and tested on 138 samples. Conclusions The findings identify a race-specific diagnosis method for prostate cancer detection using enhanced feature selection and machine learning. This approach emphasizes the potential for developing unbiased diagnostic tools in specific populations.

Abstract licence: CC BY

1. 9.

   Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 · 2014

2. 18.
   Corrected

   Splicing mutations in human genetic disorders: examples, detection, and confirmation · 2018

3. 25.

   Array programming with NumPy · 2020

44 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.