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Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron:

Adithama Mulia1, David Agustriawan1, Marlinda Overbeek1

  • 1Department of Informatics, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, A Bldg, 5th Fl, Tangerang, 15810, Indonesia, 62 87781535936.

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Summary
This summary is machine-generated.

This study developed a DNA methylation classifier for prostate cancer staging, achieving high accuracy in White patients. However, the model performed poorly in minority groups, underscoring the need for race-specific approaches in cancer detection.

Keywords:
DNA methylationdifferentially methylated positionsexplainable artificial intelligencefeature selectionmultilayer perceptronprostate cancerrace-aware model

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Area of Science:

  • Genomic Medicine
  • Computational Biology
  • Oncology

Background:

  • Prostate cancer progression varies significantly due to biological and racial factors.
  • DNA methylation shows promise for early cancer detection, but its use in machine learning across diverse populations is limited.

Purpose of the Study:

  • To develop a prostate cancer stage classifier using DNA methylation data and a multilayer perceptron (MLP) model for a predominantly White cohort.
  • To evaluate the model's performance on other racial groups to highlight the need for race-specific models.

Main Methods:

  • Processed TCGA-PRAD data using differentially methylated position (DMP) analysis to identify CpG sites correlated with cancer stages.
  • Refined features using recursive feature elimination (RFE) and trained MLP models.
  • Employed SHAP and LIME for model interpretation and identification of key DNA methylation features.

Main Results:

  • The best model achieved 95% accuracy and 99% AUC on White training data using 90 features.
  • Model performance significantly declined in racial minority groups due to sample imbalance and race-specific methylation patterns.
  • Feature importance analysis revealed specific CpG sites driving model predictions.

Conclusions:

  • A race-aware MLP model for prostate cancer staging using DNA methylation data was developed and optimized.
  • SHAP and LIME confirmed the predictive relevance of selected CpG sites, enhancing model transparency.
  • The study highlights the critical need for race-specific modeling strategies in cancer classification due to performance disparities across racial groups.