This article attempts to predict the rupture risk in anterior communicating artery (ACOM) aneurysms by using a two-layer feed-forward artificial neural network (ANN). To improve ANN efficiency, an adaptive synthetic (ADASYN) sampling approach was applied to generate more synthetic data for unruptured aneurysms. Based on the results, the conclusion is that this ANN presents good performance and offers a valuable tool for prediction of rupture risk in ACOM aneurysms, which may facilitate management of unruptured ACOM aneurysms.
- A feed-forward ANN was designed for the prediction of rupture risk in ACOM aneurysms.
- Two demographic parameters, 13 morphological aneurysm parameters, and hypertension/smoking history were acquired.
- An ADASYN sampling approach was used to improve ANN quality.
- Overall prediction accuracy of 94.8% for the raw samples was achieved.
Authors: Jinjin Liu, Yongchun Chen, Li Lan, Boli Lin, Weijian Chen, Meihao Wang, Rui Li, Yunjun Yang, Bing Zhao, Zilong Hu and Yuxia Duan