1. [Tree-Augmented NaÏve Bayesian network model for predicting prostate cancer].
- Author
-
Xiao LH, Chen PR, Li M, Gou ZP, Xiang LC, Li YZ, and Feng P
- Subjects
- Biopsy, Humans, Male, Predictive Value of Tests, Prostate, Prostate-Specific Antigen blood, Sensitivity and Specificity, Bayes Theorem, Prostatic Neoplasms diagnosis
- Abstract
Objective: To evaluate the integrated performance of age, serum PSA, and transrectal ultrasound images in the prediction of prostate cancer using a Tree-Augmented NaÏve (TAN) Bayesian network model., Methods: We collected such data as age, serum PSA, transrectal ultrasound findings, and pathological diagnoses from 941 male patients who underwent prostate biopsy from January 2008 to September 2011. Using a TAN Bayesian network model, we analyzed the data for predicting prostate cancer, and compared them with the gold standards of pathological diagnosis., Results: The accuracy, sensitivity, specificity, positive prediction rate, and negative prediction rate of the TAN Bayesian network model were 85.11%, 88.37%, 83.67%, 70.37%, and 94.25%, respectively., Conclusions: Based on age, serum PSA, and transrectal ultrasound images, the TAN Bayesian network model has a high value for the prediction of prostate cancer, and can help improve the clinical screening and diagnosis of the disease.
- Published
- 2016