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Genomics and Applied Biology
2014, Vol.5, No.3
http://gab.sophiapublisher.com
Research Article
Open Access
Classification of Prostate Cancer with the Use of Artificial Immune System and
ANN
Hasibe Cingilli VURAL
1
,
and Seral ÖZŞEN
2
1. Selcuk University, Department of Biology, Molecular Biology, 42079 Selçuklu, Konya, Turkey
2. Selcuk University,Department of Electrical and Electronics Engineering, 42079 Selçuklu, Konya, Turkey
Corresponding Author email:
hcingilli@selcuk.edu.tr
Genomics Appied Biology, 2014, Vol.5, No.3 doi: 10.5376/gab.2014.05.0003
Copyright
© 2014 VURAL1 and ÖZŞEN. This is an open access article published under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Before analyzing cells in Laboratory in prostate cancer detection, a classification system can give valuable information
about the cancer. The purpose of this paper is to assess the value of Artificial Immune System (AIS)
and Artificial Neural Networks
(ANN) for classification of prostate cancer cases. Paraffine-embedded prostate cancer tissue specimens of 50 prostate cancer subjects
were used in this study. Age range was 35-72 years and all subjects were males. 10 subjects had family history of cancer and 40
patients were non family. An Artificial Immune System (AIS) which is based on clonal selection theory was used to classify these 50
subjects as healthy and patient. With the correct arrangement in system parameters, AIS has reached a classification accuracy of
93.33%. This ratio in 50 data means that in test phase, only one data was misclassified as healthy whereas indeed that data was
belonging to a patient. The classification procedure was also done with another method which is a well-known effective classification
method for biomedical data: Artificial Neural Networks. The result for this application was 100% with ANN method. While it seems
that there is a big difference in the performances of AIS and ANN in the classification accuracy, this difference was only because of 1
data. Thus, it can be said that, AIS is also a good performing classification algorithm as well as ANN for this application.
Keywords
Prostate cancer classification; Artificial immune system; Artificial neural networks
Introduction
Prostate cancer is the most common malignancy in
men and majority leading cause of cancer deaths in
the Eastern world. The genetic predisposition to
prostate cancer is well established, as genomic
instability is a common feature of many human
cancers. Epidemiological studies have suggested that
several risk factors (Li, et al., 1997; Steck et al., 1997;
Magnusson et al., 1998; Marshall, 1991). Knowing
about the genetic markers of prostate cancer in men
with prostate cancer diagnosis could help. Inactivation
or deletion a large number of tumor-related genes,
which otherwise regulate normal cellular growth and
suppression of abnormal cell proliferation, is
recognized to be one of the major mechanisms of
tumorigenesis (Goddard and Solomon, 1993; Waite
and Protean, 2002). The successful treatment of
prostate cancer relies on detection of the disease at its
earliest stages. Although prostate-specific antigen
(PSA)-based screening has been a significant advance
in the early diagnosis of prostate cancer, identifying
specific genetic alterations in a given family or patient
will allow more appropriate screening for early
disease. Mapping and identification of specific
prostate cancer susceptibility genes is slowly
becoming a reality.
Immune System (IS) can be regarded as a defence
mechanism of the body. It seeks the condition of body
and explores if there is any dangerous situation. If so,
the related units are put into effect and necessary
processes are held. Artificial Immune System (AIS) is
an artificial intelligence method whose roots lie to
simple mathematical models developed for the
understanding of natural immune system [(L. N. de
Castro, and Timmis, 2002; L.N. de Castro, and Von
Zuben, 1999; Dasgupta, 1998). Later, with properties
like learning, memory, distributed and organized
working, etc. in these models, researchers had begun
to scrutinize the natural immune system as an
inspiration source of AIS. Since that time, many
methods modelling or inspiring from some metaphors
in natural immune system have been developed and