Download yapay bağişiklik si̇stemi̇ i̇le kadinlarda

Diagnosis of Iron Deficiency Anemia in Women With Artificial Immune
System
Nilüfer Yurtay1, Cengiz Sertkaya1
Computer Engineering Department, Sakarya University, Sakarya, Turkey
[email protected], [email protected]
Abstract
Iron deficiency anemia is a common type of anemia and affects women more often than men. Blood
tests are important for diagnosis. In this study, an Artificial Immune System-based(AIS) medical
application has been developed for diagnosing iron deficiency based anemia in women. Developed
system performance result was measured with 98% accuracy and 93.65% sensitivity. These results
shows that AIS can be utilized to diagnose anemia in women.
Keywords: Artificial Immune System (AIS), Iron Deficiency Anemia
1. Introduction
There is an increased number of studies on biological based systems in recent years. Common aspect
of theese studies is that they are inspired by the systems full filling vital functions of living organizms.
The immune system, like any other system, performs tasks such as pattern recognition, learning,
memory management, diversity creation, generalization, recognition and optimization that are carried
out by complex cells, molecules and organs. Many calculation technics based upon immune principles
aim not only to understand existing system but also to solve many engineering problems[1]. Artificial
Immune Based Systems are developed to achieve the same goal.
The artificial immune systems have been introduced as a biological based computational method in
1990s[2]. It has a learning algorithm which has been inspired of human body capability of recognition
and destruction of germs. The learning has been done with the process of lymphocyte activities,
natural antibody production, tolerance, memory, and etc. This system produces results with antibody,
antigen, affinity and threshold value concepts[3]. AIS has a layered structure. The first layer of each
system is the application layer. A suitable representative is selected that compounds for this area.
Affinity measurement is calculated with Hamming or Euclidean distances. The next layer contains the
algorithms that determine system behavior. Shape space is obtained from algorithms and contains
immune cells and molecules[4].
AIS is tried and the results are discussed in many areas; computer security [5,6], optimization
problems [7,8], the shop scheduling [9.10], production line control [11], the autonomous robot system
[12], among others.
In recent years, AIS has been started studying in medicine. AIS is used for the diagnosis of thyroid
and led to a classification success rate of 95.90% [14]. Another study for the diagnosis of thyroid, a
fuzzy artificial immune system is discussed. The success of the classification of 85% was obtained
[15]. In another study, most important chest diseases such as bronchitis, asthma and tubercolis are
diagnosed with a high success rate of 94% [16].
The success rate is higher AIS techniques. For this reason, studies in this area continue to increase.
In this study, the diagnosis of iron deficiency anemia in women is tested and the results are described
for the AIS and is compared with ANN.
2.Anemia
Anemia is a decrease in number of red blood cells (RBCs) or less than the normal quantity of
hemoglobin in the blood. if untreated, it can cause many heart disease. Body functions of patients
with anemia do not work regularly. This situation increases the mortality rate of patients with anemia.
Iron deficiency anemia, is defined as a reduction in blood red cells and low iron content may cause a
decrease in red blood cells[17].
According to 2005 statistics from Turkey Statistical Institute(TUIK), the number of patients that is
hospitalized because of anemia is 30.117 [18]. This is just the number of patients receiving inpatient
treatment. It is estimated that more than the number of patients in outpatient settings. One of the most
important cause of anemia is known as iron deficiency [17]. Values, which were due to iron deficiency
anemia in the United States for the years 1999-2000, expressed as 12% in women between the ages
of 12-49, with 9% for 50-69 years of age, 6% for 70 and up [19]. These rates were lower than in
children and men.
3.Method
3.1 Data Set
In this study, Zonguldak State Hospital in 2010 laboratory results were used. Data of 2600 female
patients, are discussed. Dataset contains 567 anemia and 2073 no-anemia data and has 6 attributes
and 2 types of classification knowledge.
Attributes of dataset have been given below in Table 1. Another study for the same data, artificial
neural networks(ANN: FFN, CFN, DDN,TDN,PNN VE LVQ) were used and results were analyzed[20].
Table 1- Hematological Attributes of iron deficiency anemia [20,21]
Attribute
Explanation
RBC
Red Blood Cells
HGB
Hemoglobin
HCT
Hematocrit
MCV
Mean Corpuscular Volume
MCH
Mean Corpuscular Hemoglobin
MCHT
Mean Corpuscular Hemoglobin Concentration
3.2 Artificial Immune System (AIS )
Values
4,5-6
12-16
36-48
80-100
27-34
31-37
AIS systems have many different structures of different algorithms technique. Theese techniques are
known as negative selection technique, a positive selection and clonal selection [13]. Clonal selection
based shape-space representation principle is used in diagnosis of anemia in this study. The general
structure of AIS shown in Fig. 1.
TRAINING
TEST
Dataset of Anemia
AIS
KNN Algorithm
Test results
Fig.1 The model of AIS
Developed AIS structure consists of the following five-steps :

A permanent population is generated by reading training data. • The important AIS parameters
such as healthy measure (Affinity), threshold value, the number of cloned cell and maximum
number of cloning are determined. These parameter’s values were chosen as shown in Table
2.
Table 2 AIS Parameters
Parameter
E
A
C
Cmax

Description
Threshold value
Affinity
The number of cloned cell
Max. number of cloning
Value
0,5
0,5
2
10
Two samples belonging to the same class in permanent population are randomly selected.
New antibody is generated by performing cloning procedure on these selected samples.
Antibody is added into the temporary population. This step is performed on random samples
of the same class until it reaches the maximum number of cloning. The Cloning process is as
fig. 2
Attributes
1
2
3
4
5
3.66 6.42 23.62 64.52 17.52
6
27.12
1.Antibody
2. Antibody
4.24 6.62 23.72 55.92 15.62
27.82
4.24 6.62 23.62 55.92 17.52
27.82
Temporary
Antibody
Fig. 2 Cloning proses

Affinity measure is applied for each created sample of temporary population. Each samples
are checked by the Euclidean approach whether or not included in the same class that were
produced. If it is closer to the same class, this sample is added to permanent population,
othervise it is destroyed. Used Affinity measure and Euclidean formula are calculated as
follows.
Euclidean ( x, y) 
n
 (x  y )
i 1
i
2
i
Wherein n is the number of attributes, x permanent antibody, y is temporarily antibody
expression vector.
Affinity measure( A)  1  Euclidean
n
 (x  y )
i 1

i
2
i
If the number of samples in permanent population is changed, the cloning step is repeated. In
the absence of change in the number of samples, the training process has been completed.

After completion of the training process, as the next step classification of test data should be
initialized. Accordingly, each test sample is compared with samples of a permanent population
as a result of the AIS algorithm by using KNN algorithm. The similarity values are calculated
with KNN algorithm. The test sample is added to the same class which has the highest
similarity in permanent population. The formulas that are used to calculate the sum of
similarity and the similarity as follows.
k j  a j  bj
n k  e e  k
j
j j
j
d (a)   
j 1  k j  e j e j
Wherein k is measure of linear distance, a is processed antibody , b is compared antibody, e is
threshold and d represents total similarity value between antibody(a) and antibody(b). Similarity value
is calculated by comparing with all the other antibodies. Antibody is added to other antibody’s class
which has the highest similarity.
3.3 Experimental Results
Proposed method for the solution of the problem is AIS system which works on clonal selection based
shape-space representation principle . As dataset in the [20] study, 2600 amount of data which
included 2000 training and 600 test datas is used. AIS is trained with training dataset. The remaining
600 data were used as input in order to test the system been trained and the produced outputs of AIS
system was observed. 600 amount of data is used as input in order to test the AIS system. This
dataset has 478 data of healty and 122 data from patients who have been diagnosed with anemia.
The formed system structures by [20] study and the ROC analysis results of this study are shown in
Table 3.
Table 3: Comparison AIS with ANN
Classifier
TP
TN
FP
FN
Accuracy (%)
Sensitivity (%)
FFN
118
473
4
5
99.16
96.06
CFN
117
472
5
5
98.95
96.06
DDN
118
473
4
3
99.16
97.60
TDN
118
474
4
4
99.16
96.82
LVQ
119
450
3
28
99.33
81.33
PNN
115
469
7
9
98.52
93,12
AIS
118
470
4
8
98,00
93,65
[20]
This
study
ROC analysis components TP, true positive, TN, true negatives, FP, false positive and FN, false
negative were used for the evaluation of obtained test results.
The accuracy and sensitivity values in the table that test results are calculated as follows [22,23]..
TP
(TP  FN )
Sensitivity 
Specifity 
TN
(TN  FP)
Accuracy 
TP  TN
(TN  TP  FP  FN )
The obtained test results have been proven that developed AIS system can learn the problem and has
a good performance to achieve the desired outcomes.
4.Conclusions
The success of using Artificial Intelligence techiques in medical diagnostic decision support systems is
higher classical methods. In this study, the iron deficiency anemia was diagnosed by using artificial
immune system.The results of AIS are compared with same data studied artificial neural network
techniques FFN, CFN, DDN, TDN, LVQ and PNN.
According to test results, AIS can be used to diagnose anemia in women due to iron deficiency. The
AIS system which is working on clonal selection based shape-space representation principle, was
solved the problem with 98% high rate accuracy.
5. Acknowledgment
Thank you to chief physician and directorate of computer center personell of Zonguldak State
Hospital, also special thanks to Ziynet Yılmaz and M.Recep Bozkurt for assistance to acquisition and
use of data.
References
[1] De Castro, L.N., Von Zuben, F.J., Artificial Immune Systems: Part I-Basic Theory and Applications,
Technical Report, TR-DCA 01/99, December 2009.
[2] Nasaroui, O., Dasgupta, D., Gonzales, F., The Promise and Challenges of Artificial Immune
System Based Web Usage Mining: Preliminary Results, Workshop on Web Analytics at Second SIAM
International Conference on Data mining(SDM), Arlington, VA, April 11-13 2002.
[3] Garrett, S.,M., How Do We Evaluate Artificial Immune Systems?, Evolutionary Computation, MIT
Press, 13(2): 145-178
[4] Leandro N. De Castro,Jonathan Timmis, Artificial immune systems: a new computational
intelligence approach, Springer, 357 pages, 2002.
[5] Forrest, S., PERELSON, A., ALLEN, L., Self-Nonself Discrimination in a Computer, Proceedings of
the IEEE Symposium on Research in Security and Privacy: 202-212, 1994.
[6] Hofmeyr, S.A., Forrest, S., Immunity by Design: An Artificial Immune System, Proceedings of the
Genetic and Evolutionary Computation Conference (GECCO), San Francisco, CA, 1289-1296, 1999.
[7] De Castro, L.N., Von Zuben, F.J., Learning and optimization Using the Clonal Selection Principle,
In the Special Issue on Artificial Immune Systems of the Journal IEEE Transactions on Evolutionary
Computation,June 6(3), 2000.
[8] De Castro, L.N., Von Zuben, F.J., The Clonal Selection Algorithm with Engineering Applications,
GECCO 2000, Las Vegas, Nevada, USA, July 8, 2000.
[9] Jensen, M., Hansen, T., Robust solutions to job shop problems, October 2007.
[10] Hart, E., Ross, P., Nelson, J., Producing Robust Schedules via an Artificial Immune System,
ICEC, 464-469, 1998.
[11] Mori, K., Tsukiyama, M.,Fukuda, T., Artificial Immunity Based Management Systems for a
Semiconducter Production Line, In IEEE International Conference on Systems, Man and Cybernetics,
1: 852-856, 1997.
[12] Lee, D., Jun, H., Sim, B., Aritificial Immune System for Realization of Cooperative Strategies and
Group Behaviour in collective Autonomous mobile Robots, Proceedings of 4th Int. Symp. On Artificial
Liefe and Robotics: 232-235, 1999.
[13] Sertkaya, C., Immune System in Computer Security, Master Thesis, Institude of Natural Sciences,
Sakarya University, 2009.
[14] Kodaz, H., Özşen,S.,Arslan,A., Güneş,S., Medical application of information gain based artificial
immune recognition system (AIRS): Diagnosis of thyroid disease. Expert Systems with Applications,
36:3086–3092,2009.
[15] Polat, K., Şahan, S., Güneş,S., A novel hybrid method based on artificial immune recognition
system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis. Expert Systems with
Applications, 32:1141–1147,2007.
[16] Temurtas, F., Er, O., Sertkaya, C., A Comparative Study on Chronic Obstructive Pulmonary and
Pheumonia Diseases Diagnosis using Neural Network and Artificial Immune System, Journal of
Medical System, 33:485–492, 2009.
[17] Linker, C.A., Current Medical Diagnosis & Treatment , Chapter 13 ,page 470, 2003
[18] http://www.tuik.gov.tr,2011.
[19] Susan F. Clark. Iron Deficiency Anemia,Nutrition in Clinical Practice, American Society for
Parenteral and Enteral Nutrition,23(2):128-141, 2008.
http://ncp.sagepub.com/content/23/2/128.full.pdf+html.
[20] Yılmaz, Z., Bozkurt, M.R., Determination of Women Iron Deficiency Anemia Using Neural
Networks, Journal of Medical System, http://dx.doi.org/10.1007/s10916-011-9772-4 ,2011.
[21] Özaslan, E.,Delibaşı, T.(2008), Tusem Book,Tusem Publisher, 46-48, 2008.
[22] ROC Analysis, 2012. Available at http://tr.wikipedia.org/wiki/ROC
[23] ROC Analysis, 2012. Available at http://en.wikipedia.org/wiki/Sensitivity_and_specificity