Page 173 - Big Data Analytics for Intelligent Healthcare Management

166 CHAPTER 6 CLASSIFICATION FRAMEWORK OF fMRI DATA

Table 6.11 Number of Samples and Features
Number of Samples
(in both SP and PS)
No of Features
Subject Class P Class S No of Voxels No of Snapshots (Voxels*Snapshots)
04799 25 25 1874 18 29,984
04820 25 25 1888 18 30,208
04847 25 25 1713 18 27,408
05675 25 25 2239 18 35,824
05680 25 25 2230 18 35,680
05710 25 25 1883 18 30,128

6.3.6.3 N-most active-based feature
In this experiment, the N-most active voxels were chosen so that most energize PS+SP whose feature
size was 700 18¼12,600. Table 6.14 presents the performance of the experiment where the N-most
active voxels were considered. It can be seen that the accuracy was the best compared to previous ex-
periments. Feature standardization improved accuracy in LR but it remained the same in SVM.

6.3.6.4 Most active ROI-based feature
In Table 6.15, the most active voxels inside each ROI were used to reduce the dimensionality of the
feature vector. For the PS dataset, the LR showed more accuracy compared to the SVM. In the SP ex-
periment, the SVM showed more precision than LR.

6.4 RESULT ANALYSIS
6.4.1 SUMMARY OF THE SUBJECT-DEPENDENT RESULTS
Better results were found when the N-most active voxels were used in the above cases, where the SP
data results were better than PS. Table 6.16 shows that data standardization improved accuracy in most
of the cases but in some cases, it slightly decreased the performance. It was also seen that in SVM, after
applying standardization, performance was almost same or slightly increased.

6.4.2 SUBJECT-INDEPENDENT EXPERIMENT
In Table 6.17, we only considered seven ROI, which were specified by experts. Since, in the ROI av-
erage, we considered the average of all the voxels present within the seven ROI, the feature size was
7 16¼112, but in the case of active ROI average, we considered the average of 100 most active vox-
els within each of the seven ROI. In this case, the feature size was 7 16¼112. Table 6.18 showed that
the performances achieved by subject-independence were substandard to subject-dependent ones,
which is as was expected. And as feature values varied with subjects, data standardization is very im-
portant and we can see that after applying this, accuracy improved.