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Related Experiment Video

Updated: Jul 2, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Gene Expression-Based Cancer Classification for Handling the Class Imbalance Problem and Curse of Dimensionality.

Sadam Al-Azani1, Omer S Alkhnbashi2, Emad Ramadan2

  • 1SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia.

International Journal of Molecular Sciences
|February 24, 2024
PubMed
Summary

Early cancer detection is crucial. This study enhances gene expression analysis for cancer classification by addressing class imbalance and high dimensionality, achieving 100% accuracy with SVM-SMOTE and random forests.

Keywords:
cancer detection and diagnosisclass imbalancefeature selectiongene expression

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cancer is a leading global cause of death, often diagnosed late due to conventional methods.
  • Early cancer detection and diagnosis are critical for improving patient survival rates.
  • Gene expression microarray technology offers promising results for early cancer detection.

Purpose of the Study:

  • To address class imbalance and the curse of dimensionality in gene expression-based cancer classification.
  • To evaluate the effectiveness of oversampling techniques and feature selection methods.
  • To propose a combined feature selection technique (CHiS and IG) for identifying significant genes.

Main Methods:

  • Utilized oversampling techniques to handle class imbalance by generating synthetic samples.
  • Applied chi-square and information gain for feature selection to mitigate the curse of dimensionality.
  • Developed and evaluated a combined feature selection method (CHiS and IG) and compared it with individual techniques.
  • Investigated the performance of various oversampling techniques with ensemble-based learning, including SVM-SMOTE and random forests.

Main Results:

  • Oversampling techniques generally improved classification results across four benchmark datasets.
  • The proposed combined feature selection technique (CHiS and IG) outperformed individual methods in most cases.
  • SVM-SMOTE combined with the random forests classifier achieved 100% accuracy, surpassing existing literature findings.

Conclusions:

  • The study demonstrates the effectiveness of oversampling and combined feature selection for accurate cancer classification from gene expression data.
  • The proposed methods significantly improve early cancer detection and diagnosis capabilities.
  • Achieving 100% accuracy highlights the potential of these advanced computational approaches in oncology.