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

David Agustriawan1, Adithama Mulia1, Marlinda Vasty Overbeek1

  • 1Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Scientia Garden Jalan Boulevard Gading Serpong, Tangerang, 15810, Indonesia, 62 877-8153-5936.

JMIR Bioinformatics and Biotechnology
|December 4, 2025
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Summary
This summary is machine-generated.

This study developed a race-specific machine learning model for prostate cancer detection using gene expression data. The model achieves high accuracy in diagnosing prostate cancer in both White and African American patients.

Keywords:
classificationfeature selectiongene expressionmachine learningprostate cancerrace specificsupport vector machine

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Previous machine learning models for prostate cancer detection using gene expression data achieved high accuracy but overlooked racial diversity and outlier gene selection.
  • Gene expression profiles are crucial for understanding prostate cancer heterogeneity.

Purpose of the Study:

  • To develop a race-specific classification method for diagnosing prostate cancer using gene expression data.
  • To address the limitations of previous studies by incorporating racial diversity and advanced feature selection.

Main Methods:

  • Utilized differentially expressed gene analysis, receiver operating characteristic analysis, and MSigDB verification for feature selection.
  • Constructed support vector machine (SVM) models using selected gene features.
  • Evaluated model performance based on classification accuracy for different racial groups.

Main Results:

  • A model with 139 gene features achieved 98% accuracy for White patients and 97% for African American patients.
  • Another model using only 9 gene features demonstrated strong performance with 97% accuracy for White patients and 95% for African American patients.
  • Results highlight the effectiveness of race-specific models in prostate cancer detection.

Conclusions:

  • The study successfully identified a race-specific diagnostic method for prostate cancer detection.
  • Enhanced feature selection and machine learning approaches can lead to unbiased diagnostic tools for specific populations.
  • This research paves the way for more personalized and accurate prostate cancer diagnostics.