title: Prognostic factors: classification approaches in patients with lung cancer.
abstract: With the proliferation of potential prognostic factors for lung cancer, it is becoming increasingly more difficult to integrate the information provided by these factors into a single accurate prediction of clinical outcome. Here we reviewed five classification methods for their capabilities in classification of 200 patients with lung cancer into distinct prognostic groups using survival outcome as a criteria. The source of patient data for this study is a Lung Tumour Registry from Institute for Lung Diseases, University Clinical Hospital, Belgrade. Almost all developed classification algorithms determined prognostic groups according to biochemical tumour markers LDH and alkaline phosphatase, producing most significant split, instead of commonly used staging variables. The choice of which approach to use for a given classification problem depends not only on statistical properties of method, but also on medical considerations, such as whether more differential findings are given greater weight and the applicability of a classification rule.
authors.affiliation: Institute of Social Medicine, Statistics and Health Research, Medical Faculty, Belgrade.
authors.forename: D
authors.initials: D
authors.lastname: Stanisavljević
authors.forename: D
authors.initials: D
authors.lastname: Jovanović
authors.forename: J
authors.initials: J
authors.lastname: Marinković
authors.forename: M
authors.initials: M
authors.lastname: Milosavljević
mesh_terms: D000469:Alkaline Phosphatase / Q000097:blood; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000097:blood; D005103:Expert Systems; D006801:Humans; D007770:L-Lactate Dehydrogenase / Q000097:blood; D008175:Lung Neoplasms / Q000145:classification* / Q000401:mortality / Q000473:pathology; D008432:Mathematical Computing; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D011379:Prognosis; D016019:Survival Analysis