Page 159 - Big Data Analytics for Intelligent Healthcare Management

152 CHAPTER 6 CLASSIFICATION FRAMEWORK OF fMRI DATA

and velocity. The volume refers to amount of the data, velocity is the rate with which data is changing,
and variety is the type of the data, and the different ways to use the data.
Big data is a dataset that is so large and composite that conventional data processing software is not
able to handle it. These data are both structured and unstructured and are widely used in various fields
on a day-to-day basis. Big data analytics enables the rapid examination of data to significantly reduce
costs and time and thus help in uncovering hidden patterns, unknown correlations, market trends, and
other constructive information that can aid smart decision-making [5].
fMRI is an essential neuroimaging technique. It compares the activities of the brain by recording the
flow of oxygenated blood supply. It produces an enormous amount of data that have to be evaluated,
and thus also generates a vast network of outcomes. The existing medical image processing tools
are not competent enough to integrate resources efficiently. Big data analytics platforms have been
developed for huge datasets such as fMRI. Big data analytics helps in knowledge extraction and
interpretation of the fMRI dataset.
The traditional statistical data analytic solutions generally focus on static analytics that are
restricted to the analysis of samples that are stationary in time, which often leads to untrustworthy con-
clusions. Machine learning is a good option that addresses these problems. It focuses on the progress of
quick and proficient algorithms for real-time processing of data. The main goal of machine learning is
to create exact predictions of various types.
Machine learning is an application that provides the facility to learn and improve from experience
with no involvement, support, or human intervention and adjust actions accordingly [6]. The develop-
ment of learning starts with observations or data, such as understanding training examples to look for
patterns in data and make enhanced decisions in the future. Machine learning uses various methods to
analyze data and broadly group the data into two types: supervised and unsupervised. In supervised
learning, trained examples are used to make predictions. The training dataset includes the input data
and their desired output values and it can make predictions of the given values for new examples. Su-
pervised learning contains two types of algorithms that is, classification and regression. Unsupervised
learning employs a dataset without previous training. Cluster analysis is the most familiar unsupervised
learning method on the basis of similarity evaluated using metrics such as Euclidean or probabilistic
distance, where the datasets are grouped into various clusters.
fMRI has provided researchers with countless new insights into the inner workings of the human
brain. The statistical analysis of fMRI data is very difficult. The data is enormous in volume, compris-
ing a sequence of MRI. These data are heterogeneous and noisy due to possible head movement or
breathing of subjects and lack of proper technology. The traditional statistical data analytic solutions
are not efficient enough to draw effective conclusions from such high-resolution measurements. In the
machine learning approach, the dimensionality of the data is effectively reduced by removing redun-
dancy through voxel elimination. The voxels from seven selected region of interest (ROI) are chosen as
features for classification. By using machine learning, we can predict or classify the fMRI dataset ef-
ficiently. We explore here two classifier training methods: logistic regression and support vector ma-
chine [5]. Logistic regression is the regression problem, with a small number of distinct values for
prediction. Classification can accept only two values that lie between 0 and 1.
We could move toward the classification without paying attention the detail that y is discrete-
valued, and apply previous linear regression to attempt to calculate y given x. We know that y 2
{0, 1} and thus hθ(x) must take values between 0 and 1.