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156 CHAPTER 6 CLASSIFICATION FRAMEWORK OF fMRI DATA

formed excellent support in radiation oncology. The enhancement of the predictive model performance
will contribute to their increased usage in personalizing treatment through ionizing radiation safely and
efficiently.
The use of Spark-based machine learning techniques on streaming of big data was discussed by
Lekha R. et al. [20]. Decision tree algorithms were used. Decision tree is the most common machine
learning method for classification and was selected for prediction. Spark’s machine learning library
was employed to build a flexible machine learning model designed for prediction, which was compe-
tent at efficiently handling massive datasets. Lina Zhoua et al. [21] identified the prospects and chal-
lenges of using machine learning techniques on big data. A framework using machine learning was
introduced for big data. The machine learning component is the key component for identifying hidden
patterns from big data. The other components are the user, domain, system, and big data. The machine
learning part handles the challenges as well as extraction of knowledge for decision-making.
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A method based on machine learning was discussed by Alvaro Brando ´n Herna ´ndez et al. [22] for
optimization of parallelism within applications using big data. By observing the various metrics of the
system and its application, it is possible to achieve optimal configuration by avoiding chances of fail-
ure, deterioration of performance, and boosts in resource utilization. Various regression algorithms,
such as LR, gradient boosting regression, and support vector regression were used. Along with these
algorithms, k-neighbors were also used to predict the optimal set-up.
Lei Zhang [4] explored the machine learning methods to differentiate drug-users from healthy per-
sons. 3D brain images of the candidate were acquired with fMRI BOLD. With the help of machine
learning methods, the hidden pattern for differentiating the subjects addicted to drugs from nonaddicted
controls were found and used to carry out classification for diagnosis.
Mehdi Behroozi et al. [23] processed the fMRI data in order to trace the changes in brain activities
due to injury of the brain. The mechanisms that were employed for the analysis of fMRI data are dis-
cussed. The preprocessing stages, including univariate and multivariate techniques as employed in
functional MRI data evaluation, are described. Guo-Rong et al. [24] proposed that by using BOLD,
signal peaks can provide significant information in the resting-state fMRI. In the framework of infor-
mation theory, partial conditioning was applied to a restricted subset of variables. The differences be-
tween BOLD and combined BOLD level effective networks were compared.
AnettaLasek-Bal. et al. [25–27] assessed the brain processes in fMRI in patients with strokes due to
interruptions of blood supply to the brain and evaluated the possible relationship between the order of
activity and the neurological status. The fMRI was carried out and patients were observed on first day
as well as on the 14th day after the stroke. Disparity was perceived in stroke and nonstroke hemi-
spheres. More than half of the patients with stroke showed cerebellar activation. Paul M. Matthews
et al. [26] discussed the clinical concepts emerging from fMRI functional connectomics. Their work
includes the exploration of different challenges and possible opportunities for clinically pertinent ap-
plications of fMRI-based functional connections. fMRI had important influences on clinical concepts
guiding analysis and management of patients.
A model for steady multiple subject independent component analysis (ICA) on fMRI datasets was
discussed by G. Varoquaux et al. [28]. Vincent Michel et al. [29] proposed a method that merges signals
from several brain regions found in fMRI to forecast the behavior of the control for the period of a
scanning session. The length of the probable spatial configurations was condensed to a single tree
adapted to the signal. Then the tree was reduced in a supervised setting. Reduction of dimensionality
was accomplished with the help of feature agglomeration and the constructed features offer a