title: Assessment of Breast Cancer Risk in an Iranian Female Population Using Bayesian Networks with Varying Node Number
abstract: Objective: As a source of information, medical data can feature hidden relationships. However, the high volume
  of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and
  processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of
  Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian
  networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and
  clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the
  receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive
  values were used to evaluate discriminative performance. Result: A network incorporating selected features performed
  better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference
  among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively
  discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals.
  Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be
  more readily be applied in clinical settings.
authors.affiliation: Colorectal Research Center, Shiraz University of Medical Sciences. Shiraz, Iran. Email: msepandi@gmail.com
authors.forename: Abbas
authors.initials: A
authors.lastname: Rezaianzadeh
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Sepandi
authors.forename: Salar
authors.initials: S
authors.lastname: Rahimikazerooni
keywords: Breast cancer; Bayesian networks; risk assessment