title: Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data.
abstract: BACKGROUND
  Prognostic studies of breast cancer survivability have been aided by machine learning algorithms, which can predict the survival of a particular patient based on historical patient data. However, it is not easy to collect labeled patient records. It takes at least 5 years to label a patient record as 'survived' or 'not survived'. Unguided trials of numerous types of oncology therapies are also very expensive. Confidentiality agreements with doctors and patients are also required to obtain labeled patient records.
  
  
  METHODS
  These difficulties in the collection of labeled patient data have led researchers to consider semi-supervised learning (SSL), a recent machine learning algorithm, because it is also capable of utilizing unlabeled patient data, which is relatively easier to collect. Therefore, it is regarded as an algorithm that could circumvent the known difficulties. However, the fact is yet valid even on SSL that more labeled data lead to better prediction. To compensate for the lack of labeled patient data, we may consider the concept of tagging virtual labels to unlabeled patient data, that is, 'pseudo-labels,' and treating them as if they were labeled.
  
  
  RESULTS
  Our proposed algorithm, 'SSL Co-training', implements this concept based on SSL. SSL Co-training was tested using the surveillance, epidemiology, and end results database for breast cancer and it delivered a mean accuracy of 76% and a mean area under the curve of 0.81.
authors: Department of Industrial Engineering, Ajou University, Suwon, South Korea.
authors: Juhyeon
authors: J
authors: Kim
authors: Hyunjung
authors: H
authors: Shin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D010363:Pattern Recognition, Automated; D057285:Precision Medicine; D011379:Prognosis; D016019:Survival Analysis
keywords: Breast Cancer Survivability; Co Training; Machine Learning; Semi Supervised Learning