Comparison of Bayesian network and binary Logistic Regression methods for prediction of prostate cancer

Author

Bozkurt, Selen ; Uyar, Asli ; Gulkesen, Kemal Hakan

Author_Institution

Fac. of Med. Dept. of Biostat. & Med. Inf., Akdeniz Univ., Antalya, Turkey

Volume

3

fYear

2011

fDate

15-17 Oct. 2011

Firstpage

1689

Lastpage

1691

Abstract

Prostate cancer is one of the most common cancers in men. Luckily, Serum PSA level, age, digital rectal examination (DRE), and clinical symptoms are helpful for early detection of this tumor. The aim of this study was to examine and compare the methods used for improving the diagnostic accuracy of serum PSA in Turkey, a country with low incidence of prostate cancer. The predictors used for early detection of prostatic carcinoma were identified by both Logistic Regression and Bayesian networks. The results of the methods were compared in terms of predicting performance and advantages.

Keywords

belief networks; cancer; logistics; medical diagnostic computing; patient diagnosis; regression analysis; tumours; Bayesian network; binary logistic regression methods; digital rectal examination; prostate cancer; prostatic carcinoma; serum PSA level; tumor; Bayesian methods; Biopsy; Educational institutions; Logistics; Medical diagnostic imaging; Prostate cancer; Bayesian Networks; Logistic Regression; Prostate Cancer;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-9351-7

Type

conf

DOI

10.1109/BMEI.2011.6098546

Filename

6098546