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An Expert System for Predicting the Cervical Cancer using Data Mining

Techniques

Bargana Benazir1, A.Nagarajan

2

1Research Scholar,

2Assistant Professor

Department of Computer Applications, Alagappa University,

Karaikudi-600 003, Tamilnadu, India.

ABSTRACT

Cervical cancer is one of the leading causes of death for middle-aged women in the

developing world, yet it is almost completely preventable if precancerous lesions are identified

and treated promptly. There are different methods for control and prevention of cervical cancer

which include conventional cytology (Pap smear), liquid-based cytology, Human Papilloma

Virus (HPV) screening, and vaccination against HPV. With more and more biological

information generated, the most pressing task of bioinformatics has become to analyze and

interpret various types of data. Data mining techniques are emerging in recent years due to the

processing and manipulation of the healthcare data. In this paper, the data mining techniques like

Feature selection, classification techniques are utilized to predict cervical cancer.

KEYWORDS: Cervical Cancer, Feature Selection, Genetic Algorithm, Artificial Neural

Network

1. INTRODUCTION

The second common cancer that appears in all age women groups is cervical cancer. In

fact, the annual global statistics of World Health Organization (WHO) estimated that 470 600

International Journal of Pure and Applied MathematicsVolume 118 No. 20 2018, 1971-1987ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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new cases and 233400 deaths from cervical cancer have happened around the year 2010 [1]. As

reported in National Cervical Cancer Coalition (NCCC), cervical cancer is a cancer of the cervix

which has commonly caused by a virus named Human Papilloma Virus (HPV) [2]. This cancer

can be cured if it is detected and treated in its earlier stage. It usually develops in the cervix of

the women. The screening method which is manually done for cervical cells collection in the

cervix region of the uterus is the Pap smear test. Cervical cancer can also be usually found early

by having regular screening with this Pap test. Pap smear means human cells samples stained by

the so-called Papanicolaou method. Being alert to any signs and symptoms of cervical cancer can

also help avoid unnecessary delays in diagnosis [3]. Early detection dramatically improves the

chances of successful treatment and prevents any early cervical cell changes from becoming

cancerous. It is better to have a regular screening of cervical cancer because women with early

cervical cancer and pre-cancers usually have no symptoms, and symptoms will arise only after

the invasive growth of cancerous cells. But for most of the cases, it throws symptoms only in the

advanced stages.

The traditional first visual procedures like, either the physician or doctor uses a brush or

spatula to collects the cervical cells manually [4]. Then, these cells which are obtained are sent to

the pathologist in the laboratory in a sealed container for classification of the standard and

abnormal cervical cells. It is impossible for a handful pair of eyes to sit and screen every woman

on the planet, where it leads to an error in classification and thus, this method results in high

false positive rates and it is also very costly as the pathologist can examine only 4 to 5 slides per

day. The next traditional method used for cervical cancer screening involves immersing the

collected cervical cells in the 5% acetic acid liquid, namely Liquid Cytology-based (LAB)

method [5]. Here, the three types of tissues such as Squamous epithelium (SE), Columnar

Epithelium (CE), and Aceto white region (AW) are used to classify the cervical cells. If the

collected cervical cell samples are abnormal, the AW tissue region changes its color to white.

Thus, the LCB method found to be providing accurate results when compared to the previous

method.

The automated approach is required to reduce the false positive rate and to classify the abnormal

cells if any quickly and perfectly. Here, we proposed a kind of automated method which includes

pre-processing technique, where the median filter is used to remove the noises in the cell, and

International Journal of Pure and Applied Mathematics Special Issue

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then the features are extracted using GLCM (gray level co-occurrence matrix). The genetic

algorithm which acts as an optimizer is employed for feature subset selection, and finally, Neural

Network classifier is used for the classification of cervical cancer cells as normal and abnormal.

2. RELATED WORKS

To classify the cervical cells automatically the first ANN with Back-propagation (BP)

method is proposed by Mehdi et al. [6], where the slides of the cervical cells are classified into

mild cancer cells, moderate cancer cells and severe cancer cells. During 1998, the cervical cancer

cells were detected using radial basis function (RBF) networks was introduced by Tumer et al.

[7]. The author presented experimental results for both Multi-layer Perceptron( MLP) network

and RBF network. A hybrid approach for the detection of cervical cancer cells was introduced in

2000, where the knowledge-based rule in knowledge-based neural networks was applied to

extract the cancer image. Then, the cervical cancer cells are detected by calculating the pixel

value of an image using the genetic algorithm. It has also used MLP [8]. From the input Pap

smear slides, the features are extracted and then fuzzy rules are applied for classifying the normal

and abnormal cells was introduced by Zhong Li et al. [9]. The cervical cancer cells are predicted

using the new ANN-based method was developed by Takashi et al. [10]. The biopsy tissues are

examined using the combination of histology radiations with the feed-forward ANN. Nikolaos

proposes a new second-order neural network classifier for the classification of Pap smear

cervical cancer cells classification. The non-linear squares are tested using LMAM (Levenberg –

Marquardt with adaptive Momentum) [11]. From the uterine cervical region, the cell region is

extracted using the nucleus segmentation and recognition method which is proposed by Kim et

al. [12]. The extracted RGB space image is converted into HSI, and it is used as the input layer in

fuzzy RBF network. The output layer which is connected with the middle is classified into NNL,

WNL, SCC, LSIL, where WNL normal and others are abnormal. SCC is the cancer cells. The

features of the nucleus and cytoplasm which is extracted are used with the RBF to classify

cervical cancer from the Pap smear slides was developed by Francisco et al. [13]. In [14], the

new Infrared features are added into the MLP network to extract the cervical features for the

cancer cell classification from the thin Prep. Royan et al. [15] propose an ANN and Learning

Vector Quantization (LVQ) classify the normal and abnormal cells of cervix region. This method

includes the image processing modules namely, pre-processing, filtering and feature extraction.


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The input image is stored in ANN, and from the extracted features, the normal and abnormal

cells are detected using the LVQ method with an accuracy rate of 90%. In [16], a gene

programming is used to compare the MLP, RBF and probabilistic NN. In Gene programming,

the chromosomes are used as linear strings of fixed length. The gene structure is organized as

head and tail. The gene length depends on the head size, the MLP used comprises of some layers

(i) input has the features, hidden layer has the predefined number of nodes, and the output layer

is composed of response neurons. The technique proposed in [17] uses the MLP in ANN to

perform the thinking. As ANN provides better understanding and results, this ANN architecture

consists of one input and one output without any restriction in the number of hidden layers.

Babak system is mainly targeted towards a novel Pap smear test to overcome the inefficiency in

the older system due to the inexperience pathologist and cells overlapping [18]. There, the

cervical cancer cells were diagnosed using MLP which involves two phases such as, image

processing and feedforward MLPNN. The Levelberg-Marquardt Feed forward MLP NN was

used to classify the un-segmented cell features as normal, LSII, HSIL cells with high accuracy.

Phatak et al. introduced a new method for detecting cervical cancer using the Support Vector

Machine (SVM) and ANN [19].

3. DATASET DESCRIPTION

The following table 1 represents the dataset that is used throughout this work.

Table 1: Description of the Dataset

Feature

Index

Feature Name

1 (int) Age

2 (int) Number of sexual partners

3 (int) First sexual intercourse (age)

4 (int) Num of pregnancies

5 (bool) Smokes

6 (bool) Smokes (years)

7 (bool) Smokes (packs/year)

8 (bool) Hormonal Contraceptives


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9 (int) Hormonal Contraceptives (years)

10 (bool) IUD

11 (int) IUD (years)

12 (bool) STDs

13 (int) STDs (number)

14 (bool) STDs:condylomatosis

15 (bool) STDs:cervicalcondylomatosis

16 (bool) STDs:vaginalcondylomatosis

17 (bool) STDs:vulvo-perinealcondylomatosis

18 (bool) STDs:syphilis

19 (bool) STDs:pelvic inflammatory disease

20 (bool) STDs:genital herpes

21 (bool) STDs:molluscumcontagiosum

22 (bool) STDs:AIDS

23 (bool) STDs:HIV

24 (bool) STDs:Hepatitis B

25 (bool) STDs:HPV

26 (int) STDs: Number of diagnosis

27 (int) STDs: Time since first diagnosis

28 (int) STDs: Time since last diagnosis

29 (bool) Dx:Cancer

30 (bool) Dx:CIN

31 (bool) Dx:HPV

32 (bool) Dx

33 (bool) Hinselmann: target variable

34 (bool) Schiller: target variable

35 (bool) Cytology: target variable

36 (bool) Biopsy: target variable


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4. PROPOSED FRAMEWORK

The following figure 1 depicts the framework for the classification of cervical cancer by

using two stages, 1) feature selection stage and 2) classification stage. In the pre-processing

stage, Genetic Algorithm is utilized to reduce the size of the feature space. In the classification

stage, Artificial Neural Network (MLP) is used to classify the dataset into two categories normal

and Abnormal.

Figure 1: Proposed Framework for Predicting the Cervical Cancer using Data Mining

4.1 Pre-processing by using Genetic Algorithm

Genetic Algorithm (GA) is a primary Heuristic Algorithm, and it is an evolutionary

algorithm. It has to find the optimal solutionin the process of natural selection and crossover. As

shown in Fig.2, theprocess of GAs is as follows. Its randomlygenerates first individuals to form

an initial population. Each consistsof a capable Gene that represents a result of the given problem

and encoded byChromosomes. Depending on the specific optimization issues, differentencoding

methods are employed. For example, the binary or real-valuedrepresentation can be used to


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express schemata. The representation choosing canhave a significant impact on the successful

application of GAs. Anobjective function is designed to express the problem in Genetic

Algorithms, and thefitness value is obtained by applying each to the object.

Figure 2: Process of Genetic Algorithm

Genetic Algorithm design is to include the following three essential operators: Selection,

Crossover, and Mutation. The selection operator is the process oftaking individuals the next

generation from the present generation. The selectionoperator is commonly designed to select

probabilistically better solutions (individualswith high Fitness Values) and remove other bad

solutions. The crossover operatoris the action of gene recombination which recombines two

parent’s chromosomesto generate new individuals to be utilized in the next generation. For

example, there are the following main crossover techniques: the one is one-point crossover and

second is the two-pointcrossover. The mutation operator is the action of changing one or more

gene valuesrandomly chosen in the current chromosome. The generational action based

ongenetic operators is repeated to gradually evolve candidate solutions whichconverge on


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approximate solutions more and more. When the Genetic Algorithmprocess is terminated due to

the given constraints, the optimum of the solutions isobtained to solve the problem.

For example, a large number of features are mostly generated by using terms extracted

from the given corpus. In Genetic Algorithms, it is too difficult toperform feature selection due to

its runtime complexity and iteration. Therefore,we haven’t done feature selection in the single

GA process. That is, we havegenerated the partial features of fixed length through the GA

process and groupedeach partial feature to obtain a final feature set. When the multiple GA

processesare performed, various problem domains can be explored, and genetic diversity can be

ensured.

Algorithm: Feature Selection Procedure

cdsFeaLength: The number of features in Cervical Dataset

lSubFeature: The number of sub feature

lFinalFeature: The number of final feature set

maxGen: Maximum generation

limitTime: Limit of elapsed Time

popsize: Population Size

// length of chromosome = iSubFeature

// value of gene = index of feature(1 < index <cdsFeaLength)

while(lFinalFeautre> length of finalFeautre) {

p <-initialPopulation(cdsFeaLength, iSubFeature, popsize)

computeFitness(p)

generation = 1

while(maxGen>= generation OR limitTime>ElapsedTime) {

newPop<- linearRankSelection(p)


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pbxCrossover(newPop)

mutate(newPop)

p <- newPop

computeFitness(p)

ElapsedTime = ElapsedTime+ nowTime

generation = generation + 1

}

subFeature<- TopFitnessFeature(p)

finalFeautre<- addFeature(subFeature)

}

returnfinalFeature

4.2 CLASSIFICATION BASED ON ARTIFICIAL NEURAL NETWORK

A Neural Network (NN) model which is the branch of Artificial Intelligence (AI) is

generally concerned to as Artificial Neural Networks (ANNs). ANN teaches the method to

execute task, instead of programming computational method to do definite tasks. To perform this

tasks, AI system is generated. It is a pragmatic model which can precisely and quickly find the

patterns forgot in data that replicate practicable knowledge. One case of these AI model is NN.

AI methods should discover from data on a constant basis. In the areas of medical diagnosis

relationships with different data, the most available techniques are the AI techniques.

With a single, sufficiently more hidden layer, it is potential to represent any continuous method

of the inputs with arbitrary accuracy. Regrettably, for any particular network system, it is harder

to characterize precisely which functions can be represented and which ones cannot. As a

consequence, given particular learning issues. It is unknown how to select the right number of

hidden units in advance. One usually resorts to cross-validation, but this can be computationally

expensive for large networks. We need to conceive multiple output units for multi-layer

networks system. Let (x, y) be a single sampling with its hope output labels y = {y1,...,yi,...,yM}.


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The error at the output units is justly y hW(x), and we can use this to correct the weights between

the hidden layer and the output layer. The above steps produce a term equivalent to the possible

error at the hidden layer, i.e., the possible error at the output layer is back-propagated to the

hidden layer. It is subsequently used to update the weights between the input units and the

hidden layer.

Step 1: To update the weights between the hidden and output layers.

Let Erri be the i-thcomponent of the error vector y - hW(x)

Define ∆𝑖 = 𝐸𝑟𝑟𝑖 𝑋 𝑔′(𝑖𝑛𝑖)

The weight-update pattern becomes𝑊𝑗 ,𝑖 ← 𝑊𝑗 ,𝑖 + 𝛼 𝑋 𝑎𝑗 𝑋∆𝑖 this is similar to weight-

updates for perceptrons

Step 2: Back-propagate the errors to the hidden layer.

The idea is that the hidden node j is "responsible" for some divide of the error ∆𝑖 in every

output nodes.

Thus the division error ∆𝑖values are divided according to the strength (weight) of the

joining between the hidden node and the output node:

∆𝑖= 𝑔′ 𝑖𝑛𝑗 𝑊𝑗 ,𝑖∆𝑖

𝑖

Step 3: Update the weights (W) between the input units and the hidden layer.Again, this is

similar to weight-updates in Perceptrons:

𝑊𝑘 ,𝑗 → 𝑊𝑘 ,𝑗 + 𝛼 𝑋 𝑎𝑘 𝑋 ∆𝑗

For the general case of multiple hidden layers:

Step 1: Compute the ∆ values for the output units, using the observed error.

Step 2: Starting with the output layer, iterate the following for every layer in the

network system until the earliest hidden later is reached:

Propagate the ∆ values back to the previous layer.


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Update the weights between the two layers.

Step 3: Repeat Steps 1 to 2 for all training samples.

The following is the back-propagation algorithm for learning in the multilayer networks.

Step 1: function BACK-PROP-LEARNING(examples, network) returns a neural network

Step 2: inputs:,, a set of examples, each and every with input vector x and output vector

ynetwork, a multilayer network with L layers, weights Wj, I, activation function g.

Step 3: repeat

Step 4: for each e in examples do

Step 5: for each node j in the input layer do aj ← xj [e]

Step 6: for l =2 to M do

Step 7: 𝑖𝑛𝑖 ← 𝑊𝑗 ,𝑖𝑎𝑗𝑗

Step 8: 𝑎𝑖 ← 𝑔(𝑖𝑛𝑖)

Step 9:for each node i in the output layer do

Step 10: ∆𝑖 ← 𝑔′ 𝑖𝑛𝑖 𝑋 (𝑦𝑖 𝑒 − 𝑎𝑖)

Step 11: for ℓ = M − 1 to 1 do

Step 12: for each node j in layer ℓ do

Step 13: ∆𝑗 ← 𝑔′ 𝑖𝑛𝑗 𝑊𝑗 ,𝑖∆𝑖𝑖

Step 14: for each node i in layer ℓ + 1 do

Step 15: 𝑊𝑗 ,𝑖 ← 𝑊𝑗 ,𝑖 + 𝛼 𝑋 𝑎𝑗 𝑋 ∆𝑖

Step 16: until some stopping criterion is satisfied

Step 17: return NEURAL-NET-HYPOTHESIS(network)


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5. RESULT AND DISCUSSIONS

The implementation tools like Weka and MATLAB are used in this research work. The

following table 1 represents the number of features obtained by using the Genetic algorithm,

Particle Swarm Optimization, and Ant Colony Optimization.

Table 2: Number of Features selected by using Genetic Algorithm, Particle Swarm

Optimization, and Ant Colony Optimization

Feature

Index

Genetic Algorithm Particle Swarm

Optimization

Ant Colony

Optimization

1 Age Age Num of pregnancies

2 Hormonal Contraceptives Num of pregnancies Smokes

3 Hormonal Contraceptives

(years)

Smokes Smokes (years)

4 pelvic inflammatory disease Smokes (years) Smokes (packs/year)

5 genital herpes condylomatosis Hormonal

Contraceptives

6 Hepatitis B cervical

condylomatosis

Hormonal

Contraceptives (ages)

7 Number of diagnoses vaginal

condylomatosis

condylomatosis

8 Time since the first diagnosis pelvic inflammatory

disease

cervical

condylomatosis

9 Time since the last diagnosis genital herpes vulvo-perineal

condylomatosis

10 Hepatitis B AIDS syphilis

11 HPV HIV pelvic inflammatory

disease

12 Hepatitis B genital herpes

13 HPV

14 Number of diagnoses


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Time since the first

diagnosis

Figure 2: presents the result of training, validation and testing samples. Mean squared

error (MSE) and regression (R)

The following table 2 represents the Mean Square Error (MSE) and Regression Values

for the training, validation, and testing of the cervical cancer dataset by using Artificial Neural

Network. This table depicts the result obtained from the pre-processing stage using Genetic

Algorithm, Particle Swarm Optimization.


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Table 3: The Mean Square Error (MSE) and Regression values for thetraining, validation,

and testing

Dataset Genetic Algorithm Particle Swarm Optimization

MSE R MSE R

Training 5.11986e-8 9.99999e-1 1.2365e-13 7.65326e-3

Validation 2.85475e-7 9.99999e-1 5.45126e-12 6.78952e-6

Testing 1.13132e-6 9.99997e-1 4.22451e-10 4.784124e-7

6. CONCLUSION

Medical data mining is specific because of the unique nature of medical data. The

medical data is specific concerning the values the attributes may take. Very often these attributes

are binary. They denote presence or absence of some features, e.g., symptoms, diagnoses. In this

work, a framework is proposed to predict cervical cancer among the women. The Genetic

algorithm is used as the feature selection technique in the pre-processing tasks and Artificial

Neural Network with MLP backpropagation learning algorithm is used to classify the patients

into two normal and abnormal.

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[1] http://www.who.int/mediacentre/factsheets/fs380/en/

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