Evaluation of risk factors and survival rates of patients with early-stage breast cancer with machine learning and traditional methods.

Journal: International journal of medical informatics
Published Date: Jul 11, 2024

Abstract

BACKGROUND: This article is aimed to make predictions in terms of prognostic factors and compare prediction methods by using Cox proportional hazards regression analysis (CPH), some machine learning techniques and Accelerated Failure Time (AFT) model for post-treatment survival probabilities according to clinical presentations and pathological information of early-stage breast cancer patients.

Authors

  • Emrah Gökay Özgür
    Marmara University, School of Medicine, Department of Biostatistics, Turkiye. Electronic address: emrahgokayozgur@gmail.com.
  • Ayse Ulgen
    Department of Mathematics and Physics. School of Science and Technology. Nottingham Trent University. United Kingdom. Girne American University, Faculty of Medicine, Department of Biostatistics, Cyprus.
  • Sinan Uzun
    Marmara University, Institute of Health Sciences, Department of Biostatistics, Turkiye.
  • Gülnaz Nural Bekiroğlu
    Marmara University, School of Medicine, Department of Biostatistics, Turkiye.

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