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Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database

Author

Listed:
  • Ruiyang Wu
  • Jing Luo
  • Hangyu Wan
  • Haiyan Zhang
  • Yewei Yuan
  • Huihua Hu
  • Jinyan Feng
  • Jing Wen
  • Yan Wang
  • Junyan Li
  • Qi Liang
  • Fengjiao Gan
  • Gang Zhang

Abstract

Introduction: Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance. Objective: The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others. Methods: This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database. Results: Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820–0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P

Suggested Citation

  • Ruiyang Wu & Jing Luo & Hangyu Wan & Haiyan Zhang & Yewei Yuan & Huihua Hu & Jinyan Feng & Jing Wen & Yan Wang & Junyan Li & Qi Liang & Fengjiao Gan & Gang Zhang, 2023. "Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-15, January.
    • Handle: RePEc:plo:pone00:0280340
    DOI: 10.1371/journal.pone.0280340
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    References listed on IDEAS

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    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
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