title: Neural network analysis of employment history as a risk factor for prostate cancer.
abstract: BACKGROUND
  Prostate cancer is the most common non-cutaneous malignancy in men. Its etiology likely involves environmental exposures and demographic factors.
  
  
  OBJECTIVE
  Investigate the potential relationship between occupation history and prostate cancer risk in a population-based, case-control study (n=1365).
  
  
  METHODS
  The variables: race, age group, smoking status, income, marital status, education and the first 15 years of employment history were examined by sequential odds ratio analysis then compared to a neural network consensus model.
  
  
  RESULTS
  Both the sequential odds ratio method and the neural network consensus model identified a similar hypothetical case of greatest risk: a Black, married man, older than 60 years, with at best a high school diploma who made between $25,000-$65,000. The work history determined by odds ratio analysis consisted of 10 years in the chemical industry with 3 yrs in the processing plant. Neural network analysis showed a similar work history with 8 years in the chemical industry and 2 years in the processing plant.
  
  
  CONCLUSIONS
  Neural network outcomes are similar to sequential odds ratio calculations. This work supported previous studies by finding well known demographic risk factors for prostate cancer including certain processing jobs and chemical related jobs.
authors: Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI 48201, USA. DombiG@Karmanos.org
authors: G W
authors: GW
authors: Dombi
authors: J P
authors: JP
authors: Rosbolt
authors: R K
authors: RK
authors: Severson
mesh_terms: D044383:Black People; D016022:Case-Control Studies; D004651:Employment; D004781:Environmental Exposure; D006801:Humans; D007182:Income; D008297:Male; D008393:Marriage; D016571:Neural Networks, Computer; D016017:Odds Ratio; D011471:Prostatic Neoplasms / Q000209:etiology*; D012307:Risk Factors