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A 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 Jl. Boulevard Gading Serpong, Tangerang, ID.

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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 achieved high accuracy in diagnosing prostate cancer in both white and African American patients.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Machine learning models for prostate cancer detection show high accuracy.
  • Previous studies often overlook racial diversity and outlier gene selection in gene expression data.

Purpose of the Study:

  • To develop a race-specific classification method for prostate cancer diagnosis using gene expression data.
  • To address limitations in prior studies by considering population diversity and robust feature selection.

Main Methods:

  • Utilized Differentially Expressed Gene (DEG) analysis, Receiver Operating Characteristic (ROC) analysis, and MSigDB verification for feature selection.
  • Constructed Support Vector Machine (SVM) models for classification.

Main Results:

  • A model with 139 gene features achieved 98% accuracy for white and 97% for African American patients.
  • A model with only 9 gene features demonstrated strong performance with 97% accuracy for white and 95% for African American patients.

Conclusions:

  • Identified a race-specific diagnostic method for prostate cancer detection.
  • Highlights the potential of enhanced feature selection and machine learning for unbiased diagnostic tools in diverse populations.