Biomedical Engineering Reference
In-Depth Information
1 Introduction
Nowadays, humans are suffering from many health problems. This century
s most
progressive disease is HIV/AIDS and its problems. HIV infects only humans; the
de
'
'
s immune system, which normally protects the body
against illness and is a virus. Viruses are tiny substances that enter the body
ciencies lie in the body
s cells
and cause illness. After the virus enters the body, there is a period of rapid viral
replication, leading to a drastic change of virus in the peripheral blood. During
primary infection, the level of HIV may reach several million virus particles per
milliliter of blood. This response is accompanied by a marked drop in the number of
circulating CD4 T cells. Controlling virus levels, which peak and then decline, as
the CD4 T cell counts recover. A good CD8 T cell response has been linked to
slower disease progression and a better prognosis, though it does not eliminate the
virus.
Arti
'
eld of study devoted to the devel-
opment of computational models based on the behavior of the biological immune
system, applied to several Engineering and Computer Science problems. Some of
its applications include pattern recognition, fault and anomaly detection, data
analysis, agent-based systems, scheduling, machine learning, control and autono-
mous navigation, search and optimization methods, arti
cial Immune Systems (AIS) is a new
cial life, and information
systems security [
1
]. AIS can be regarded as collection of algorithms which are
abstracted from natural immune system such as HIS [
2
]. AISs have been applied to
anomaly detections [
3
,
4
].
The Genetic algorithm (GA) was
rst introduced by John Holland and his team
in the University of Michigan in the early 1960s [
5
]. It is widely used to solve many
optimization problems in all
elds of engineering and sciences [
6
]. These algo-
rithms are hypothetically and empirically proved to be giving ef
cient search in
complex space. GAs are very useful particularly when the size of the dataset is very
large. Knowledge engineering can be used to solve a diversity of tasks such as
clustering, classication, and regression and association discovery [
7
]. Association
rule mining is one of the widely used approaches for constructing the computational
intelligence techniques. A classi
cation rule is represented in the form: If P then Q,
where P is a combination of predicting attribute values and Q is the predicted class.
Varma et al. [
8
] GA for better diagnosis of diabetes disease using association
rule mining. In this computational intelligence technique, we tested the performance
of the method using the Pima Indian Diabetes (PID) dataset taken from UCI
machine learning repository.
Basheer et al. [
9
] a GA-based approach for mining classi
cation rules from large
database is presented and used for emphasizing on accuracy, coverage, and com-
prehensibility of the rules and simplifying the implementation of a GA. The design
of encoding, genetic operators, and
tness function of GA for this task is discussed.
Pradhan et al. [
10
] a multi-class genetic programming (GP)-based classi
er
design will help the medical practitioner to con
rm his/her diagnosis toward pre-
diabetic, diabetic, and non-diabetic patients.
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