This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68%) when applied on SPCDB dataset for comparison on patients with OCD NOCD conditions. SVM (75%) represents a promising alternative to Partin table from which pathology staging can benefit.
Studies in health technology and informatics. 2017 Jan [Epub]
Jae Kwon Kim, In Hye Yook, Mun Joo Choi, Jong Sik Lee, Yong Hyun Park, Ji Youl Lee, In Young Choi
Department of Computer Science and Information Engineering, Inha University, InhaRo 100, Nam-gu, Incheon, South Korea., Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.