## 4 Discussion  
To the best of our knowledge this is the first study to investigate the cerebrovascular changes in different parts of human brains and their role in hypertension prediction. The question raised in this study was: would the independent study of cerebral vascular changes in the anterior or the posterior compartments improve the accuracy of hypertension prediction or not compared to studying changes over the entire brain. Answering this question will help clinicians in determining which part of human brains is more vulnerable (anterior, posterior, or the entire brain) to hypertension-related cerebrovascular changes. This could help them figure out the best protocols in dealing with this medical concern. The experimental results answered this question by proving that the changes taking place in the entire cerebral vasculature contribute to the development of hypertension and are more descriptive than changes occurring in either the anterior or the posterior compartments only. Additionally, it shows evidence that hypertension development is contributing to cerebral changes in the entire brain rather than affecting local specific compartments. One challenge in studying the change of cerebrovascular of human brains is that we  
Figure 10: Classification accuracy in whole brain, anterior, and posterior compartments for different classifiers and validation scenarios. Figure 9: Features distributions: (**a**) Averages of Gaussian curvatures, (**b**) Medians of Gaussian curvatures. cannot claim a specific cut-off for the cerebral changes that indicates whether a specific individual will be hypertensive or not. This is due to the high inter-variability between individuals' cerebrovasculars, which was considered one of the main obstacles in the road of developing accurate and automatic cerebrovascular segmentation algorithms. High inter-person variability of the vascular tree hinders the creation of a common atlas to be used for segmentation, as done for other human organs. However, in our current study, this burden is carried out and solved by the neural networks and classification algorithms. We provided these classifiers with a part of the available data along with the actual class labels for each subject. The discriminative power of these classifiers was used to investigate these data and build patterns and associations describing what a normal cerebrovascular looks like and what a hypertensive cerebrovascular looks like. After the classifiers find these patterns and associations and build their own models, we have presented new unidentified data (without class labels) to them, and they could successfully predict the actual true class labels with higher accuracy as indicated in the results section. This means that the classification algorithms could successfully use the cerebral vascular change data and find appropriate patterns for how an individual is either normal or hypertensive. Therefore, the generated classification models are considered reliable for the hypertension prediction task. Additionally, the high classification accuracy means that the cerebral features (that describe the cerebral vascular changes that lead to hypertension development) used in the classification could successfully be used to predict hypertension before its onset. Using these features and a trained classifier allows the CAD system to be able to tell whether a person with these specific cerebral vascular features a potential hypertensive patient is or not. Another limitation of our study is that the size of the MRA data was limited to 72 patients. However, this study required a long-term follow-up period of more than 700 days. This follow-up period enabled us to measure, track, and quantify cerebral vascular changes but limited the number of patients we could recruit and successfully follow-up after 700 days. In addition, and to the best of our knowledge, there are no free standard databases available in this field.