title: An application of a genetic algorithm in conjunction with other data mining methods for estimating outcome after hospitalization in cancer patients.
abstract: BACKGROUND
  We investigated which factors predicted the risk of in-hospital mortality in a general population of cancer patients with non-terminal disease and whether employing the genetic algorithm technique would be useful in this regard.
  
  
  METHODS
  A total of 201 cancer patients, including all cases of in-hospital mortality over a 2-year period, as well as a control group of subjects discharged during the same period, all having an Eastern Cooperative Oncology Group (ECOG) performance status of of < or =3 at the time of admission, were retrospectively evaluated. Indicators of in-hospital mortality were determined by multivariate logistic regression, recursive partitioning analysis, neural network, and genetic algorithm (GA) techniques. The performance of the different techniques were compared by a number of measures, including receiver operating curve (ROC) analysis.
  
  
  RESULTS
  All four analysis methods selected a combination of six explanatory variables to explain the risk of in-hospital mortality: lactate dehydrogenase (LDH), alanine transaminase (ALT), hemoglobin (Hb), white blood cell counts (Wbc), type of cancer, and reason for admission. Compared with the other 3 methods, GA selected the least number of explanatory variables, i.e. LDH and reason for admission, with similar fraction of cases explained (78.6%), and yielded a fitness score of 0.52.
  
  
  CONCLUSIONS
  LDH is an important indicator of in-hospital mortality for hospitalized cancer patients not in terminal stage. GA reliably predicted in-hospital mortality and was shown to be as efficient as the other data mining techniques employed in this study. Its use in a clinical setting for prognostication in oncology appears promising.
authors.affiliation: Department of Medical Oncology, Akdeniz University Medical Faculty, Antalya 07070, Turkey. hbozcuk@akdeniz.edu.tr
authors.forename: Hakan
authors.initials: H
authors.lastname: Bozcuk
authors.forename: Ugur
authors.initials: U
authors.lastname: Bilge
authors.forename: Emine
authors.initials: E
authors.lastname: Koyuncu
authors.forename: Hakan
authors.initials: H
authors.lastname: Gulkesen
mesh_terms: D000293:Adolescent; D000368:Aged; D000369:Aged, 80 and over; D000410:Alanine Transaminase / Q000097:blood; D000465:Algorithms; D015415:Biomarkers / Q000032:analysis / Q000097:blood; D005260:Female; D017052:Hospital Mortality; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007770:L-Lactate Dehydrogenase / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000401:mortality*; D011379:Prognosis; D015996:Survival Rate; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D007770:L-Lactate Dehydrogenase; D000410:Alanine Transaminase