Computed tomography angiography-based analysis of high-risk intracerebral haemorrhage patients by employing a mathematical model

BMC Bioinformatics

Kaikai Yin¹  Le Zhang¹  Jin Li¹  Zheng Yu²  Rong Hu³  Yujie Chen³  Zhouyang Jiang³  Hua Feng³  Tingting Li⁴ 

[1] College of Computer and Information Science, Southwest University;Department of Neurosurgery, Fuling Central Hospital;Department of Neurosurgery, Southwest Hospital, Third Military Medical University;School of Mathematics and Statistics, Southwest University;

关键词: Intracerebral haemorrhage;    Computed tomography angiography;    Ensemble learning;    Lenticulostriate arterial, data mining;   

DOI  :  10.1186/s12859-019-2741-5

来源: DOAJ

【 摘 要 】

Abstract Background Haemorrhagic stroke accounts for approximately 31.52% of all stroke cases, and the most common origin is hypertension. However, little is known about the method to identify high-risk populations of hypertensive intracerebral haemorrhage. Results The results showed that the angle between the middle cerebral artery and the internal carotid artery (AMIC), the distance between the beginning of the median artery and superior trunk (DMS), and the density (CT value) of the lenticulostriate artery (CTL) were statistically significant enough to cause intracerebral haemorrhage. In addition, we chose these three potential features for the ensemble learning classification model. Our developed ensemble-learning method outperforms not only previous work but also three other classic classification methods based on accuracy measurements. Conclusions The developed mathematical model in the present study is efficient in predicting the probability of intracerebral haemorrhage.

【 授权许可】

Unknown