Predicting Cerebral Aneurysm Rupture by Gradient Boosting Decision Tree using Clinical, Hemodynamic, and Morphological Information

Toshiyuki Haruhara, Hideto Ohgi, Masaaki Suzuki, Hiroyuki Takao, Takashi Suzuki, Soichiro Fujimura, Toshihiro Ishibashi, Makoto Yamamoto, Yuichi Murayama, Hayato Ohwada. Predicting Cerebral Aneurysm Rupture by Gradient Boosting Decision Tree using Clinical, Hemodynamic, and Morphological Information. In Gordon Lee, Ying Jin 0001, editors, Proceedings of 35th International Conference on Computers and Their Applications, CATA 2020, San Francisco, California, USA, March 23-25, 2020. Volume 69 of EPiC Series in Computing, pages 180-186, EasyChair, 2020. [doi]

Abstract

Abstract is missing.