978-1-4244-9352-4/11/$26.00 ©2011 IEEE 1689 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) Comparison of Bayesian Network and Binary Logistic Regression Methods for Prediction of Prostate Cancer Selen Bozkurt Akdeniz University, Faculty of Medicine Department of Biostatistics and Medical Informatics, Antalya, Turkey Asli Uyar Akdeniz University, Faculty of Medicine Department of Biostatistics and Medical Informatics, Antalya, Turkey Kemal Hakan Gulkesen Akdeniz University, Faculty of Medicine Department of Biostatistics and Medical Informatics, Antalya, Turkey 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-component; Prostate Cancer, Bayesian Networks, Logistic Regression I. INTRODUCTION (HEADING 1) Prostate cancer is one of the most common cancers in men [1-2]. In the United States, approximately 32,000 men have died of the prostate tumor as a part of the 217,730 men who were detected with that disease in 2010 [2]. Luckily, there are some predictors used for early detection of prostatic carcinoma. Serum PSA level, age, digital rectal examination (DRE), and clinical symptoms are helpful for early detection of this tumor [3-4]. When a patient is suspected to have a prostate tumor, a biopsy from the prostate is advised by the physician. Sometimes, because of the presence of strong indicators such as a very high serum PSA level, the decision for biopsy is easy. However, when the findings are in the grey zone, physicians and patients have to make a choice between the risk of missing an early detection of a tumor and the risk of an unnecessary biopsy [5]. There are several studies [3-4, 6] trying to establish methods to improve the sensitivity and specificity of different examinations in these grey zone cases. Generally, a PSA level between 4-10 ng/ml is accepted as having a 70% sensitivity and a 70% specificity [5, 7]. Since 1989, several concepts to further improve the diagnostic accuracy of PSA have been developed with the aim of avoiding unnecessary biopsies. Likewise, 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. II. METHOD A. Study Population All the transrectal ultrasound (TRUS)-guided prostatic biopsy cases who were admitted to Akdeniz University Hospital, Department of Urology, between January 2000-April 2007 was retrospectively evaluated. TRUS-guided biopsy could be performed only in Akdeniz University in Antalya district which has a population around 1,800,000. Original dataset included medical records of 1453 patients, samples including missing variables were excluded from the study and the remaining 983 cases, whose serum PSA level ranged between 0.05 and 1000, have been analyzed. In patients with multiple biopsies only the first one was included in the study. B. Dataset characteristics Akdeniz University Hospital Information System (HIS) and the patients’ medical records of the Department of Urology were used as data sources. HIS contains the demographic information about all patients, laboratory data of the last eight years, and pathology reports. C. Statistical Analysis Logistic Regression Analysis