title: A semi-supervised method for predicting cancer survival using incomplete clinical data.
abstract: Prediction of survival for cancer patients is an open area of research. However, many of these studies focus on datasets with a large number of patients. We present a novel method that is specifically designed to address the challenge of data scarcity, which is often the case for cancer datasets. Our method is able to use unlabeled data to improve classification by adopting a semi-supervised training approach to learn an ensemble classifier. The results of applying our method to three cancer datasets show the promise of semi-supervised learning for prediction of cancer survival.
authors.forename: Hamid Reza
authors.initials: HR
authors.lastname: Hassanzadeh
authors.forename: John H
authors.initials: JH
authors.lastname: Phan
authors.forename: May D
authors.initials: MD
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000401:mortality; D009369:Neoplasms / Q000401:mortality*; D010051:Ovarian Neoplasms / Q000401:mortality; D010190:Pancreatic Neoplasms / Q000401:mortality; D011379:Prognosis