## 3 Experimental Results
### Classification Results  
Three different experiments have been conducted in this study and were implemented using the built-in classification learner, MATLAB R2021b, using a set of linear and non-linear classifiers available with default parameters of the classification learner and with different available validation scenarios. The first experiment was conducted using the MRA scan for the entire brain. In the other two experiments, only part of the brain was studied. Only the anterior region of the brain was included in the second experiment, while the third experiment was conducted using the posterior region of the brain only. Table 1 presents the results in terms of the accuracy percentages achieved in each experiment using different classifiers with different parameters and validation scenarios. A general look at Table 1 will show that the analysis of the cerebral vascular changes in the whole brain resulted in the best accuracy in classifying the data into either normal, prehypertensive, and hypertensive. Actually, six out of the eight classification scenarios achieved the best accuracy in the whole brain experiment. In the remaining two scenarios, the first and second experiments achieved the same accuracy percentages. Classification using ensemble random forest (Bagged trees) classifier with a 15% hold-out validation scenario recorded accuracy of 90% in both the first experiment (whole brain) and the second experiment (anterior region). While using K-nearest neighbour (KNN) classifier with a distance weight (weighted) and a 10-fold validation scenario, achieved 77.8% accuracy in both the first and second experiment. No other classification scenarios in the second (anterior region) or third (posterior region) experiments outperformed the results of the first (whole brain) experiment in terms of recorded accuracy percentages.  
In addition, the accuracy percentages resulted in the second experiment (anterior region) from ensemble (bagged trees), KNN (weighted), and ensemble random undersampling boosting (RUSBoosted) classifiers were higher than accuracy percentages resulted in the third experiment (posterior region). Whereas using the KNN classifier with a cosine distance metric (cosine) resulted in 71.4% accuracy in both the second and third experiments. In contrast, the third experiment (posterior region) outperformed the second experiment (anterior region) in only two cases; using the subspace ensemble with nearest neighbor learners (subspace KNN) classifier, and the KNN with k set to equal 1 (fine) classifier with accuracy of 83.3% in both cases.  
The features used in the classification had an impact on the results as shown in Table 1. (The distributions of the vascular diameters and tortuosity features are shown in Figures 7-9). The best accuracy percentages resulted in each of the experiments were achieved using the set of features that included values to represent vascular diameter change, vascular tortuosity change, delta MAP, delta SBP, and delta DBP. The best accuracy recorded was 100% in the first experiment using ensemble (RUSBoosted) classifier, 90% in the second experiment using ensemble (bagged trees) classifier, and 83.3% in the third experiment using either KNN (fine) or ensemble (subspace KNN) classifiers. Whereas using the feature set that included only the median of vascular diameters, median of tortuosity index, delta MAP, delta SBP, and delta DBP resulted in decreased accuracy percentages in all three experiments as follows: 75%, 72.2%, and 69.4% for the first, second,