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Journal of Advances in Computer Research Quarterly pISSN: 2345-606x eISSN: 2345-6078 Sari Branch, Islamic Azad University, Sari, I.R.Iran (Vol. 7, No. 4, November 2016), Pages: 55-66 www.jacr.iausari.ac.ir

Discrimination between Iron Deficiency Anaemia (IDA) and β-Thalassemia Trait (β-TT) Based on Pattern-

Based Input Selection Artificial Neural Network (PBIS-ANN)

Mehrzad Khaki Jamei, Khadijeh Mirzaei Talarposhti

Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

[email protected]; [email protected]

Received: 2016/04/16; Accepted: 2016/06/11

Abstract

Discrimination between iron deficiency (IDA) anemia and β-thalassemia trait

(β-TT) is a time consuming and costly problem. Because, they have approximate

similar effects on routine blood test indices, in some cases, the complementary tests,

which are expensive and time consuming, would be needed for differentiate the

anemia. Complete blood count (CBC) is a fast, inexpensive, and accessible medical

test that is used as a primary test for diagnosis anemia. However, when the CBC

indices cannot exactly state the subject, more advanced tests such as electrophoresis

of hemoglobin must be performed. In this study, the CBC indices have been

considered as the inputs of classifier and the chosen architecture is pattern-based

input selection artificial neural network (PBIS-ANN). For evaluation the proposed

method, traditional methods, which are still using for the problem such as Mentzer

Index (MI), and several automated anemia diagnostic systems such as artificial

neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multi-

layer perceptron (MLP) have been compared with the proposed method. The results

indicate that the proposed method significantly outperforms the mentioned methods.

Keywords: Iron Deficiency Anemia, β-Thalassemia Trait, Artificial Neural Network, Complete

Blood Count, Hemoglobin

1. Introduction

Despite iron deficiency anemia (IDA) that is not a genetic disease, thalassemia is a

genetic trait that causes a reduction in the life span of a red blood cell [1]. Thalassemia

disease is a result of an abnormality in the genes that regulate the formation of

hemoglobin. Thalassemia screening is an action to detect the couples with the potential

of having a thalassemic infant. To achieve this goal the subjects with the thalassemia

trait of same type must be recognized. There are several types of thalassemia. When the

parents of a child have the thalassemia trait of the same type, he or she has the disease,

trait, or is normal with the probability of 25%, 50%, and 25%, respectively. One of the

most common types of thalassemia trait is β-thalassemia trait (β-TT). CBC, a fast an

inexpensive medical test, is the primary test in the thalassemia screening. The similarity

of CBC indices in IDA and β-TT makes discrimination between them so difficult.

Hence, in the subjects that couldn’t be stated with their CBC indices, the

complementary tests, which are time consuming and expensive, would be needed.

Additionally, these tests are not available in all laboratories and hospitals. As a result,

Discrimination between Iron Deficiency … M. Khaki Jamei, Kh. Mirzaei Talarposhti

56

many researchers have tried to find methods for discrimination between IDA and β-TT

with interpreting the CBC indices [2–14].

Historically, mathematical formulas were used for the problem [2–7,12,13,15]. In the

past few decades there has been increasing interested in the automated medical

diagnostic systems. Early attempts to formulate an automated diagnostic tool for anemia

classification employed image analysis [16], statistical [17], and clustering techniques

[18]. Later, the implementation protocol has shifted to the expert systems, in which both

rule-based [19–21] and hybrid neural network/rule-based [22] systems have been

successfully tested in clinical trials. The other studies on automated classification of

anemia was performed using image analysis [23] and artificial neural networks (ANN)

[24]. A comparison study on thalassemia screening in which support vector machine

(SVM), K-nearest neighbor (KNN), and an MLP were investigated [25], reported that

the MLP classifier gives slightly better results than the SVM. Another comparison study

on diagnosis IDA [26] was performed between ANN, ANFIS, and a logistic regression

model. The authors revealed that the ANN is superior to the others.

An investigation on genetic programming (GP) in thalassemia classification [27]

discovered that a GP-based decision tree and an MLP with one hidden layer are

approximately equal in accuracy. But, the MLP with two hidden layers is superior.

Piroonratana et al. investigated three classifier in hemoglobin typing for thalassemia

screening [28] that are: C4.5, random forest and MLP. They revealed that C4.5 is more

suitable than the others when using high performance liquid chromatography (HPLC) as

the input. Although, the previous studies have been successfully tested, there are two

major reasons for further investigation into automated discrimination between IDA and

β-TT. First, an increase in precision is required, because a high accuracy in thalassemia

screening is vital for having healthy population in the next generation. Second,

reduction cost and time is achievable by using the CBC indices as the inputs of the

anemia classifier, while the most accurate previous studies used the time consuming and

expensive medical tests such as the electrophoresis or HPLC. This study has been

performed to satisfy these requirements. It compares the proposed method with the

existing methods of discrimination IDA and β-TT.

The remaining of the article is organized as follows. Section 2 explains some

scientific information on the structure of hemoglobin and its formation, and the relations

between the anemia and hemoglobin. In section 3, the collected data will be analyzed.

Section 4 introduces the proposed method and section 5 compares the obtained results

of the proposed method with the other methods in literature. Finally, section 6

concludes the study and gives some suggestions for further works

2. The Effects of Anemia

Hemoglobin, which is a core component of a red the blood cell, consists of four poly

peptide chains (globin) and the other component that is called ‘heme’ [1]. First

component is affected by thalassemia and the second one is affected by IDA. There are

four types of globin chains that are: alpha (α), beta (β), gamma (γ), and delta (δ).

Regarding to the types of globin chains in the structure of hemoglobin, it can belongs to

one of the three major types of hemoglobin, including HbA (α2β2), HbA2 (α2δ2), and

HbF (α2γ2). HbF or fetal hemoglobin is the main constructor of a fetus’ red blood cells,

which after born gradually exchanges with HbA. The proportions of the HbA, HbA2,

Journal of Advances in Computer Research (Vol. 7, No. 4, November 2016) 55-66

57

and HbF in a mature and healthy person’s blood are approximately, 97%, 2-3%, and

less than 1%, respectively.

Thalassemia disease stems from the defects on the genes that regulate the formation of

globin chains. The type of thalassemia and the severity of disease are depended on the

type and the number of defective genes, respectively. In fact, persons with thalassemia

trait do not have the disease but inherit genes that cause the disease. Generally, the

incidence of β-thalassemia is more than α-thalassemia. Therefore, detection of β-TT to

avoid of having the β-thalassemia infants is a major purpose of screening. In the other

hand, when the subject has IDA disease, because of deficiency of the iron, the

concentration of the component ‘heme’, which is an iron-based molecule, will be

reduced. Hence, the quantity and quality of hemoglobin will be affected in the presence

of IDA. In the next subsection more details of the effects of anemia on blood indices

will be explained.

2.1. CBC indices

Each red blood cell contains approximately 300 million molecules of hemoglobin.

Hence, a change in the structure of globin affects the structure and functionality of the

red blood cells. As previous discussed, IDA and β-TT both lead to decrease

hemoglobin’s quality and quantity. With the difference that the first one affects the

‘heme’ component and the later affects the globin formation. As a result, thalassemia

changes the CBC indices in the following manner: Hemoglobin concentration (Hb),

which denotes the quantity of hemoglobin in blood, reduces in presence of thalassemia

disease or trait. The red blood cell count (RBC) is the other index of CBC that in a

thalassemic subject might be increased for compensation of the hemoglobin reduction.

The other significant indices of CBC that are usually employed in diagnosis thalassemia

are: mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean

corpuscular hemoglobin concentration (MCHC), red blood cell distribution width

(RDW), and hematocrit (HCT). These indices would also be reduced in the presence of

thalassemia disease or β-TT.

In the other hand, IDA has similar effects on CBC indices except that in the β-TT

subjects, we have increased RBC while in the IDA subjects the RBC index would be

decreased. This is the most important difference between those anemia. In spite of the

difference on RBC volume on different cases, it is so difficult to discriminate IDA from

β-TT subjects.

3. Data Analysis

The samples were collected from the archives of the several thalassemia screening

centers where situated in five northern cities of Iran. Total number of collected CBC

tests is 750, which were obtained from the blood specimen of 390 males in ages 20-35

and 360 females in ages 17-32 years old. The information has been organized as

follows.

I. The samples that their CBC indices obviously are in the normal ranges (218

samples).

II. The samples that their CBC indices are abnormal and definitely show

β-TT or IDA on them (98 samples, IDA = 38, β-TT = 60).


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III. The samples that their CBC indices cannot exactly determine them.

They were stated by complementary tests such as electrophoresis

(434 samples, IDA = 231, β-TT = 203).

According to the above issues total number of β-TT and number of IDA subjects are

269 and 263, respectively. Table 1 shows some examples of different categories of

collected samples. Abnormal values are shown in bold face (Table 1). It is undeniable

that the samples in the cat. III are so similar and there is a complex relation that a simple

mathematical formula cannot distinguish between IDA and β-TT subjects in this

category as well. Because, the study’s purpose is differentiate between IDA and β-TT

subjects, we excluded the normal subjects from the collected samples and the remaining

samples (532 samples including IDA and β-TT) were randomly divided into the training

and the testing patterns with 132 and 400 samples, respectively.

Table 1. Some examples of different classes of collected samples.

4. Proposed Method

ANN, which is inspired by human organism, first considered by Warren McCulloch

and Walter Pitts [29] in decade 40. After that, Donald Hebb introduced a training

algorithm for ANN. Classification, pattern recognition, prediction, function

approximation and data processing are some applications for ANNs [30–33]. In this

study a new method based on ANN has been proposed to differentiate between IDA and

β-TT subjects. In the next subsections we explain the architecture of the proposed

method.

4.1. The architecture of the proposed method

To understand the idea behind the proposed method, consider the abnormal values in

Table 1, which are marked with the symbols “↑” or “↓”. It is clear that some indices

Sample

number

RBC(M/mm3) Hb(g/dL) HCT (%) MCV(fL) MCH(pg) MCHC (%) Category

number

Type (4.5-6.3)* (13.5-18) (39-50) (80-96) (27-32) (32-38)

1 5.43 10.2 ↓ 34 ↓ 62.6 ↓ 18.8 ↓ 30 ↓ II β-TT

2 6.13 12.5 ↓ 40.1 65.4 ↓ 20.4 ↓ 31.2 ↓ II β-TT

3 6.8 ↑ 12.6 ↓ 43.4 63.8 ↓ 18.5 ↓ 29 ↓ II β-TT

4 4.73 10.1 ↓ 38.7 ↓ 69.9 ↓ 19.7 ↓ 30.3 ↓ II IDA

5 4.13 ↓ 8.2 ↓ 38.4 ↓ 71.8 ↓ 19.9 ↓ 28.9 ↓ II IDA

6 4.61 12.8 ↓ 38.9 ↓ 84.4 27.8 32.9 I normal

7 4.36 ↓ 13.1 ↓ 39.7 91.1 30 33 I normal

8 4.77 13.3 ↓ 39.7 83.2 27.9 33.5 I normal

9 3.99 ↓ 11.4 ↓ 35.1 ↓ 88 28.6 32.5 I normal

10 4.4 ↓ 13.7 40.5 92 31.1 33.8 I normal

11 6.62 ↑ 13.1 ↓ 43.9 65.9 ↓ 19.7 ↓ 29.8 ↓ III β-TT

12 4.56 10.3 ↓ 34.7 ↓ 76.1 ↓ 22.6 ↓ 29.7 ↓ III β-TT

13 5.95 12.5 ↓ 43.4 72.8 ↓ 21 ↓ 28.9 ↓ III β-TT

14 4.65 10.5 ↓ 35.6 ↓ 76.6 ↓ 22.6 ↓ 29.5 ↓ III β-TT

15 5.12 10.6 ↓ 36.7 ↓ 71.7 ↓ 20.7 ↓ 28.9 ↓ III β-TT

16 4.81 11.6 ↓ 37.6 ↓ 78.2 ↓ 24.1 ↓ 30.9 ↓ III IDA

17 5.05 11.5 ↓ 38.8 ↓ 76.8 ↓ 22.8 ↓ 29.6 ↓ III IDA

18 4.88 12.1 ↓ 38.8 ↓ 79.5 ↓ 24.8 ↓ 31.2 ↓ III IDA

19 5.03 12.7 ↓ 39.9 79.3 ↓ 25.2 ↓ 31.8 ↓ III IDA

20 5.1 13.2 ↓ 39.7 77.8 ↓ 25.9 ↓ 30.7 ↓ III IDA *Normal ranges are shown in the below of each column’s title.


59

have approximately the same patterns of abnormal values and supposed to be redundant.

Thus, to choose the most suitable indices as the inputs of the ANN, we proposed an

architecture naming pattern-based input selection artificial neural network (PBIS-ANN).

A briefly illustration of the proposed method has been shown in Fig. 1.

The dataset that were used for implementing the proposed method has been explained

in the previous section. This dataset contains 532 CBC samples from IDA and β-TT

subjects.

Figure 1. Briefly illustration of the proposed method

Table 2. The algorithm for finding the pair of CBC indices with the most similarity.

Inputs : Indices as matrix Am×n 1 & Array B in size of m1 (Bi is the lower band of normal ranges for 𝑖’𝑡ℎ

CBC index)

Output : R1 , R2 : the indices for the pair of rows with the most similarity;

1 Max = 0

2 FOR i =1 TO m-1 DO

3 FOR j=i+1 TO m DO

4 COS = Calculate similarity2 between rows i and j ;

5 IF COS > max THEN

6 max = COS ;

7 R1= i , R2 = j;

8 END IF

9 END FOR

10 END FOR 1 m is the number of indices & n is the number of samples; 2 calculate similarity regarding Eqs. (1,2);

As seen in Fig. 1, the most suitable indices of CBC are achieved with an iterative

algorithm. To achieve the goal, first, the pair of indices with the most similarity (with


60

respect to the abnormal pattern) must be found with the algorithm that is shown in Table

2. Then, one CBC index between that pair must be eliminated. To find the best choice

for elimination a coefficient that is called coefficient of interference (COI) will be

calculated with the algorithm that is shown in Table 3. The index with higher COI from

the most similar pair must be eliminated.

Table 3. The algorithm to calculate coefficient of interference (COI) for an index of the CBC tests.

Input: the values of an index of the collected CBC tests as a one dimensional array ‘I’ with the

size of N

Output: the coefficient of interference (COI) for the input ( I)

1 Insert the values of ‘I’ that are related to IDA and β-TT subjects into the

arrays A & B, respectively;

2 N1 = size of (A) & N2= size of (B) ;

4 Max1 = max of (A) & Max2 = max of (B) ;

5 Min1 = min of (A) & Min2 = min of (B) ;

6 S = 0 ;

7 IF Min1 < Min2 THEN

8 FOR i = 1 TO N1 DO

9 IF A(i) > Min2 THEN

10 S=S+1;

11 END IF

12 END FOR

13 FOR i =1 TO N2 DO

14 IF B(i) < Max1 THEN

15 S=S+1;

16 END IF

17 END FOR

18 ELSE


20 IF B(i)> Min1 THEN

21 S=S+1;

22 END IF

23 END FOR


25 IF A(i)< Max2 THEN

26 S=S+1;

27 END IF

28 END FOR

29 END IF

30 COI= S / N;

31 END.

As seen in Table 2, to find the most similar pair of indices, a coefficient that is called

coefficient of similarity (COS) must be calculated. The COS could be obtained by Eq.

(1) as follows.

1

( ) ( )

1

nk k

i i j j

k

LB Row LB Row

COSn

(1)

Where n is the number of samples, k

iRow is 'k th value for 'i th CBC index, and

( )iLB r is a function that denotes: whether r is an abnormal value or not, with respect

to the lower band of normal ranges for 'i th CBC index, and it can be obtained by Eq.

(2) as follows.


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1 '( )

0 'i

if m lower bound of normal ranges for i th CBC indexLB m

if m lower bound of normal ranges for i th CBC index

(2)

After running the proposed method, four CBC indices have survived in the final

ANN that are: Hb, RBC, HCT, and MCV. For implementation the ANN, the collected

samples were randomly divided into two groups with 132 and 400 samples for training

and test, respectively. The chosen training algorithm was Levenberg–Marquardt and the

error function was sum square errors (SSE), which could be obtained by Eq. (3) as

follows.

( ) ( ) 2

1 1

( )n m

i i

j j

i j

SSE a d

(3)

Where n and m are the number of training patterns and the number of network’s

outputs, respectively. ( )i

ja denotes the 'j th output of the network for 'i th training

pattern, and ( )i

jd represents its corresponding desired output.

4.2. Training the employed ANN

In this subsection the structure of ANN and the designing issues are explained. As

previous discussed the methodology for choosing the inputs of ANN is performed by

the PBIS algorithm. The algorithm includes several iterations. In each iteration of the

algorithm, whole procedure of designing and the test of ANN will be performed.

As previous studies have proven, the most suitable neural network for the anemia

classification problem is multi-layer perceptron (MLP) [22,25–28,34,35]. To choose the

number of layers and neurons in each layer, we used trial and error method. Finally, the

MLP with one hidden layer as shown in Fig. 2. Has been chosen as discriminator

between IDA and β-TT.

Figure 2. The employed ANN in the proposed method

As seen in Fig. 2, signed sigmoid is used as the activation function in the input layer

and the hidden layer of MLP. The output layer takes a linier function as the activation

function. Each MLP has trained several times. Fig. 3 shows the best obtained error

diagram, which is SSE function, in the training of the designed MLP.


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Figure 3. SSE diagram for training of the proposed MLP

5. Results and Comparisons

For evaluation the proposed method, we compared the obtained results with the

existing methods of discrimination IDA and β-TT. This section has been organized in

two subsections. In the first one, some traditional methods, which are still used for the

problem, have been introduced and the CBC samples were applied to them. In the

second subsection several recent works have been compared with the proposed method.

5.1. Comparison with traditional methods

Several popular traditional methods, which is still using in discrimination IDA and β-

TT, have been shown in Table 4. For better comparison, the samples that used in

evaluation traditional methods were same as the test samples of our proposed method.

Table 4. Mathematical formulas for discrimination between IDA and β-TT.

Authors Symbol Year Formula IDA β-TT

Mentzer et al. [3] MI 1973 MCV RBC 13 13

England & Fraser [2] E&FI 1973 5 3.4MCV RBC Hb 0 0

Srivastava & Bevington [4] S&BI 1973 MCH RBC 3.8 3.8

Shine & Lal [5] S&LI 1977 2 0.01MCV MCH 1530 1530

Green & King [6] G&KI 1989 2 0.01MCV RDW Hb 72 72

Sirdah et al. [12] SI 2008 3MCV RBC Hb 27 27

Ehsani et al. [13] EI 2009 10MCV RBC 15 15

In all of the experiments six popular medical indices namely: sensitivity (SENS),

specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV),

accuracy (ACC) and Youden’s index (YI), were used to comparison. The mentioned

indices are obtained by Eqs. (4 – 9) as follows:

100TP

SENSTP FN

(4)


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100TN

SPECTN FP

(5)

100TP

PPVTP FP

(6)

100TN

NPVTN FN

(7)

100TP TN

ACCTP TN FP FN

(8)

100YI SENS SPEC (9)

Where TP, TN, FP and FN are true positive, true negative, false positive and false

negative, respectively. Currently, several well-known traditional methods in the field of

diagnosis anemia (Table 4) have been compared with the proposed method. Table 5

indicates that the proposed method gives significantly better results than traditional

methods.

Table 5. Comparison between the proposed method and traditional methods for diagnosis IDA.

YI (%)

ACC (%)

NPV (%)

PPV (%)

SPEC (%)

SENS (%)

FN FP TN TP Method

84.6 92.2 90.6 93.8 93.0 91.6 18 13 173 196 MI [3]

81.2 90.5 88.1 92.7 91.9 89.3 23 15 171 191 E&FI [2]

85.6 92.7 91.1 94.3 93.5 92.1 17 12 174 197 S&BI [4]

91.1 95.5 94.2 96.7 96.2 94.9 11 7 179 203 S&LI [5]

81.6 90.7 88.6 92.8 91.9 89.7 22 15 171 192 SI [12]

84.6 92.2 90.2 94.2 93.5 91.1 19 12 174 195 EI [13]

88.5 94.2 93.6 94.8 94.1 94.4 12 11 175 202 G&KI [6]

96.1 98.0 97.3 98.6 98.4 97.7 5 3 183 209 Proposed method

Total number of samples 400 (214 IDA & 186 β-TT).

The results that are shown in Table 5 give the performance indices of diagnosis IDA.

Because, the number of β-TT subjects is depended on the number of IDA in Table 5, we

can obtain the performance indices for β-TT diagnosis with Eqs. (10 – 15) as follows:

SENS SPECTT IDA (10)

SPEC SENSTT IDA (11)

PPV NPVTT IDA (12)

NPV PPVTT IDA (13)

ACC ACCTT IDA (14)

Y I Y ITT IDA (15)

5.2. More comparisons

The second part of the tests is aimed to compare the performance of the proposed

method with some recent works. As can be observed in Table 6, the proposed method is

more accurate than the other works in this area.


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Table 6. Comparison between the proposed method and the other works.

Accuracy Specificity Sensitivity Method Year Authors

90.7 95.6 87.1 ANFIS1 2012 Azarkhish et al. [26]

96.3 95.6 96.8 ANN2 2012 Azarkhish et al. [26]

93.5 92.0 95.0 MLP3 2002 Amendolia et al. [24]

89.0 83.0 95.0 SVM4 2003 Amendolia et al. [25]

85.0 93.0 77.0 KNN5 2003 Amendolia et al. [25]

----- 91.0 93.0 RBF6 2013 Masala et. al. [35]

----- 73.0 89.0 PNN7 2013 Masala et. al. [35]

----- 91.0 80.0 KNN5 2013 Masala et. al. [35]

94.3 87.9 84.7 Math8 2015 Bordbar et al. [36]

99.5 98.4 97.7 PBIS-ANN9 Proposed method 1 adaptive neuro-fuzzy inference system; 2 artificial neural network; 3 multi-layer perceptron; 4 support vector

machine; 5 K-nearest neighbor; 6 Radial Basis Function; 7 Probabilistic Neural Network; 8 mathematical formula; 9 pattern-based input selection artificial neural network;

6. Conclusion

In this study, based entirely on fast and inexpensive blood test, we developed an

accurate medical diagnostic system for discrimination between iron deficiency anemia

(IDA) and β-thalassemia trait (β-TT). The model that is called pattern-based input

selection artificial neural network (PBIS-ANN) has been compared with traditional

methods of discrimination diagnosis IDA and β-TT and the recently published works

such as ANFIS, ANN, MLP, SVM, and KNN. The results indicate that the proposed

method significantly outperforms the mentioned methods. The proposed method stems

from a combination of human expert decision making and ANN. Before running the

proposed method the patterns of abnormal values in each input must be stated by an

expert human, on a set of inputs. The method could be employed in many medical

problems in which the medical indices are used for diagnosis a disease. Since

nationality and the ages of population, which is considered in the tests, is severely

limited. Hence, it is suggested that a wider population would be considered for the

future works.

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