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154 CHAPTER 6 CLASSIFICATION FRAMEWORK OF fMRI DATA

This method is similar to the N-most active method but here voxels are chosen uniformly from
seven ROIs. This five-feature selection approach is mainly used for subject-dependent experiments.
In subject-independent cases, we use two feature selection methods: Average ROI and Active Average
ROI where we calculate the N-most active voxels in each ROI and CalculateMean to get the super-
feature or supervoxel. The common process of calculation is to find the distribution mean and standard
deviation mean for each of the features. Then the mean is deducted from every feature and after that the
values of every feature are divided by its standard deviation.
x x
0
x ¼ (6.8)
σ
Where x represents the original feature vector, x represents the mean of that feature vector, and σ rep-
resents its standard deviation. We will use data in learning models without standardization, that is, as
we collected data from web [9], and with standardization, that is, scaling the data using the standard-
ization method. We present the results of both cases in different experiments.

6.2 RELATED WORK
In present healthcare systems, big data analytics and machine learning have become an inimitable tool
for proficient medical services. fMRI is helpful for assessing the latent risks of treatments of the brain
and how a normal, ailing, or injured brain is working. This section outlines some of the significant
contributions of different researchers in the field of machine learning, big data analytics, and fMRI
in healthcare. David M. Vock et al. [9] predicted the prospect of occurrence of different health issues
where Bayesian Networks are used for building the prediction models. Logistic regression (LR) models
were included as supplement factors. Classification is done using k-nearest neighbors.
Classification of properties of different molecules that can cause cancer or alter the genetic material
by means of machine learning techniques was proposed by N.S. Hari Narayana Moorthy [9]. Carcino-
genic and mutagenic data of 1481 chemically dissimilar particles has been used through SRD (sum of
ranking difference). MACCs fingerprinting methods are also applied to cause chemical carcinogenic-
ity. The result obtained was used to find out whether a chemical can induce cancer or can change the
genetic properties for chemical regulatory purpose. The sum of ranking difference (SRD) was com-
puted for every predictive model and used for comparison of performance.
The work of Daisuke Ichikawaa et. al [10] used substantial health check-up data and predicted
whether the candidate needed guidance related to health. A machine learning method was employed
for the purpose of identifying candidates. Five different models for prediction were developed with the
help of machine learning methods. A gradient-boosting decision tree (GBDT) was also incorporated.
J. Shotton et al. [11] presented a framework in which twin layer kernel extreme learning machine
(DKELM) was used to detect action in videos. Earlier different features were simply fused together
to improve the recognition performance, but the suggested model involved the double layer classifi-
cation with extreme learning machine. In the late fusion mechanism, the output of the early fusion layer
can be used as an input to the second layer.
Daisuke Ichikawa et al. [12] discussed the role of machine learning methods to help in effective
segregation of subjects with a possibility of hyperuricemia. They proposed a new machine learning
approach to identify candidates at high risk of hyperuricemia. The suggested system can be applied