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Performance-weighted-voting model: An ensemble machine learning method for cancer type classification using

Yawei Li1, Yuan Luo1

  • 1Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.

Quantitative Biology (Beijing, China)
|August 2, 2021
PubMed
Summary

A new performance-weighted-voting model accurately classifies cancer types using genetic data. This ensemble machine learning approach achieved 71.46% accuracy, outperforming other models and aiding cancer diagnosis.

Keywords:
cancer type classificationensemble methodlinear regressionperformance-weighted-voting modelsingle-nucleotide polymorphism

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Advancements in next-generation DNA sequencing necessitate cost reduction for genetic data acquisition.
  • Machine learning offers powerful tools for cancer analysis and diagnosis.

Purpose of the Study:

  • To develop and evaluate an ensemble machine learning model for accurate cancer type classification.
  • To improve upon existing methods for identifying cancer origins, particularly for undetermined primary sites.

Main Methods:

  • Developed a performance-weighted-voting model using five weak classifiers: logistic regression, SVM, random forest, XGBoost, and neural networks.
  • Trained and validated the model on 6,249 samples across 14 cancer types.
  • Determined classifier weights via linear regression based on cross-validation performance.

Main Results:

  • The performance-weighted-voting model achieved an overall accuracy of 71.46% using somatic mutation counts as input features.
  • This accuracy significantly surpassed individual classifiers and other ensemble methods (hard-voting, soft-voting).
  • Higher tumor mutational burden generally correlated with improved accuracy across most cancer types.

Conclusions:

  • The developed model demonstrates significant clinical utility for cancer origin identification, especially in challenging cases.
  • This study highlights ensemble systems as a robust strategy for effective cancer type classification.