title: An intelligent data mining model approach for adverse effects of hormone replacement therapy.
abstract: OBJECTIVE
  A number of controversial studies have been reported on the potential risk of breast cancer caused by hormone replacement therapy (HRT). Some studies showed a positive relationship between HRT and breast cancer onset, but other studies have not confirmed these results. To clarify the contradictory outcomes in the relationships between HRT and the onset of breast cancer, we have designed an intelligent data mining model (IDM), which is able to find proper prognostic factors for cancer onset and provides alternate measures in interpretation of outcome of clinical data through hierarchies of attributes.
  
  
  METHODS
  Based on the selection criteria, we selected 22 sets of random and case-control studies of the last 15 years, which identified any involvements of HRT with breast cancer. We analyzed the relationship between HRT and breast cancer using an IDM model consisting of data mining algorithms and public domain data mining tools. Prognostic factors which underline the major etiological dispositions of breast cancer were identified.
  
  
  RESULTS
  The variables which are closely associated with cancer onset to some degree are age 60-69, age at menopause 40-49, parity 0, age 40-49, and types of menopause oophorectomy. An implementation of IDM model on overall pooled data indicated that there is no significant relationship between breast cancer onset and HRT. It is suggested that HRT patients with specific physiological and pathological conditions related with the higher ranks of prognostic factors may have a greater chance to get breast cancer.
  
  
  CONCLUSIONS
  The results of this study may guide biomedical research directed at establishing the causal relationships between various medications and their complications, allowing an accurate assessment of efficacy and side effects of new therapeutic treatment in clinical trials without reliance on a large control population.
authors.affiliation: School of Computing and Engineering, University of Missouri - Kansas City, Kansas City, MO, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Lee
authors.forename: K
authors.initials: K
authors.lastname: Dharmala
authors.forename: C H
authors.initials: CH
authors.lastname: Lee
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000209:etiology*; D005260:Female; D020249:Hormone Replacement Therapy / Q000009:adverse effects*; D006801:Humans; D008490:Medical Informatics; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors