Rough Based Granular Computing Approach for ... - Semantic Scholar

Rough Based Granular Computing Approach for Making Treatment Decisions of Hepatitis C Farid A. Badria#1, Mohammed M.Eissa *2, Mohammed Elmogy#3, Mohammed Hashem#4 Pharmacognosy Department, Faculty of Pharmacy, Mansoura University Information Systems Department, Faculty of Computers and Information, Mansoura University Information Systems Department, Faculty of Computers and Information, Mansoura University Information Systems Department, Computer and Information Sciences, AinShams University Mansoura 35516, Egypt 1

[email protected] 3

[email protected]

4

[email protected] *

2

Mansoura 35516, Egypt

[email protected]

Abstract— Hepatitis C virus is a massive health issue affecting significant portions of the world’s population. Applying data pre-processing, feature reduction techniques and generating rules based on the selected features for classification tasks are considered as important steps in the knowledge discovery area in databases. Medical experts analyze the generated rules to find out the most significant rules to apply in order to classify unseen real life cases. This paper highlights a rough set as a powerful analysis tool based on granular computing framework to identify the most relevant attributes, generate a set of reducts which consist of a minimal set of attributes and induce a set of rules for classifying studied cases for testing new drugs for HCV treatment . The experimental results obtained, show that the overall classification accuracy offered by the proposed approach is highly based on generated rules during Hepatitis C treatment.

I. INTRODUCTION Medical informatics is the field that deals with the cognitive, information processing, and communication tasks of medical practice, education, and research, including information science and technology to support these tasks. It is an intrinsically interdisciplinary field, with a highly applied focus, but it also addresses a number of fundamental research problems in addition to planning and policy issues. It deals with the resources, devices, and methods required optimizing the acquisition, storage, retrieval, and use of in-formation in health and biomedicine fields. Medical informatics tools include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems. It is applied to the areas of nursing clinical care, dentistry, pharmacy, public health and (bio) medical research. The greater the amount of data collected, the more difficult it becomes for doctors to use the tools to analyse their own data. The tools should be applied to user-focused medical researchers to be able to analyse their own data. For example, Booth et al. [1] who discovered the differences in the temporal patterns of hepatitis B (HBV) and C (HCV). The

differences between hepatitis have not been clearly defined, and more importantly, examining whether the methods applied can work well and be applied to other fields or not. Hepatitis means “inflammation of the liver”. Hepatitis C is a liver disease caused by the hepatitis C virus (HCV) .It sometimes results an acute illness, but most often becomes a silent, chronic infection that can lead to cirrhosis (scarring), liver failure, liver cancer, and death. Generally hepatitis C can only be transmitted via infected blood. An infection is therefore possible whenever someone has come into contact with blood, dried blood or with medicines prepared from blood. There is no vaccine for hepatitis C. HCV infection is found worldwide. Countries with high rates of chronic infection are Egypt (22%), Pakistan (4.8%) and China (3.2%). These countries promote unsafe injections using contaminated equipment [2]. The World Health organization (WHO) estimates that 170 million people, i.e. 3% of the world’s population, are currently infected with the hepatitis C virus (HCV) [3]. After hepatitis C virus enters the body, it penetrates the liver cells, where it proliferates rapidly and begins to damage the cells. Our bodies contain a defence system called the immune system. It tries to combat the virus by destroying the affected liver cells. In most cases the virus becomes established in the body and cannot be cured without drugs. If hepatitis C virus is still detectable six months after the infection, the acute infection develops into a chronic infection. Patients suffering from chronic hepatitis C are at an increased risk of cirrhosis – massive damage - of the liver. Once the liver is damaged, its function gradually decreases. This condition can result liver failure and death. Once cirrhos is developed, the risk of liver cancer is also dramatically increased [4] the current treatment for HCV, according to the United Kingdom’s clinical guidelines, combines two drugs: Interferon-alpha and Ribavirin [5-7]a chief factor in prescribing combined drugs therapy is that both drugs generate side effects in most individuals. The cost of combined drugs therapy is between £3000 and £12, 000 per patient per year. A common belief states that treating patients

using expensive drugs is usually associated with potentially severe side effects which may be unsuitable unless there is clear evidence that the patient has been infected by the virus. A liver biopsy is presently the only technique available to assess HCV activity. The biopsy involves removing a small core of tissue, which is approximately 15mmin length and 2-3 mm in Diameter. This core is then set on in paraffin wax sheet, cut into pieces along its length and stained. At this level, a trained histopathologist will investigate the samples under a light microscope and use his/her practice, combined with a comprehensive definition, to evaluate the level of damage [8]. The damage can usually be classified into two types and is generally to assigned a numerical score in relation to the level of damage for each type. One of the most widely used scoring methods is the Ishak system [9], which can be summarized as: Inflammation: assigned a necroinflammatory2 (activity) score from 0 to 18. The rest of this paper is organized as follows: section 2 contains related work; Section3 gives an overview of the rough set theory and its different techniques. The Rough based Granular approach on HCV dataset is presented in Section 4. Section 5 describes the used dataset and the experimental results. Finally, the conclusion and the future progress are illustrated in Section 6. II. RELATED WORK During the last decade, a huge amount of issues relating to Hepatitis C virus (HCV) were investigated. The treatment of HCV is one of the most important issues. Many researchers have tackled clinical researches in the area of treatment of HCV and new information appears frequently. S. Wasik et al.[10] proposed a method for early-stage HCV patients’ assessment, under which predictions can be made about the efficiency of a treatment. Asselah, T. et al., [11] presented the mechanism of non-response to help overcome it and to identify any factors that can help predict the response to an anti-HCV therapy. Berenguer M., et al. [12] developed a model based on pre-and/or early post-transplantation variables, which could predict progression to severe HCV disease occurrence. Moucari R., et al. [13] evaluated the efficiency of peg interferon alfa-2b and ribavirin in unselected consecutive patients with chronic hepatitis C, treated away from trials, who were responders or non-responders to interferon and ribavirin combination. Many researchers have studied data mining using different Machine learning techniques for analysing and finding hidden patterns inside HCV patients’ datasets and predicting the response of HCV patients to treatment. Artificial neutral network has been used to predict both Human immunodeficiency virus (HIV) and HCV proteases cleavage sites and achieved high prediction accuracy [14-15]. Finding more accurate and a simpler prediction model is considered a challenging point. Wang, D. et al [16] developed three models that predict the virological response to the therapy from clinical information. They compared accuracy of artificial neural network ANN, random forests (RF) and support vector machines (SVM). Lau-Corona, D., et al. [17] constructed

Decision Trees (DTs) in patients with HCV. The recognition of clinical subgroups helped to enhance the ability to assess differences in fibrosis scores in clinical studies. Kurosaki, M.et al in [18], Hassan, M. et al. [19] developed the DT model for predicting the probability of response to therapy with PegIFN and RBV in HCV patients. Rough sets have been successfully used in HCV classification. [20]A developed approach using rough sets to discretize attribute values ,calculate set of reducts to generate decision rules for classifying presence or absence of HCV using HCV dataset is available at the UCI machine learning data repository and Contains 19 fields with one output field. However in this paper compiled from clinical trials of a newly developed medication for HCV, 28fields occur with one output field. A full description of the proposed model and a brief analysis of the results have been presented III. ROUGH BASED GRANULAR COMPUTING: A BRIEF OVERVIEW Granular computing (GrC) is [21] an umbrella term which includes theories, methodologies, techniques, and tools that make use of granules (i.e., subsets of a universe) in problem solving. A subset of the universe is called a granule in granular computing. Basic ingredients of granular computing are subsets, classes, and clusters of a universe. GrC deals with the characterization of a concept through a unit of two part ideas, the intension and extension of the concept. Recently, rough set theory and granular computation have proven to be another soft computing tool which provides robustness, a low cost solution, low computation time, and powerful in knowledge discovery. Since the GrC operations are performed on granules, rather than on the individual data points, the computation time is greatly reduced. In knowledge discovery phases, there must be some kind of support to extract the knowledge and represent any uncertainty that might occur. This fact motivated us to use the GrC models with rough sets tools to analyse HCV dataset. The rough set concept was introduced by Pawlak [22]. One of its essential merits is its direct application to classification problems founded on the assumption of that each object is associated with some information (e.g., data or knowledge)[23]. Objects characterized by the same information are said to be indiscernible in the view of available data. This induces the indiscernibly relation (equivalence relation) which is the mathematical base of rough set theory. Let U be a set of granules (universe of discourse), A be a set of attributes, and then the information system is defined as an ordered pair S= .An attributea∈ A can be regarded as a function from the domainU to some value setVa . Any decision Table of the form A = (U, A ∪ {d}), where d∈A is the decision attribute and the elements of A are called conditions attributes, and are considered as a decision system. An information system may be represented as an attribute value Table, in which granules of the universe use attributes to label rows and columns. Similarly, a decision Table may represent the decision system. Rough sets are based on the

indiscernibility relation; this indiscernibility leads to the concept of boundary-line cases, which means that some elements can be classified to concepts or their complements with the available information, and thus forms the boundaryline cases. The granule that belongs to a set with certainty is called lower approximation while upper approximation contains all granules that may possibly belong to the set, as shown in Figure1. Let I ⊆ U × U denote the indiscernibly relation on U which can be defined as: I = {((x, y) ∈ U × U ∶ b(x) = b(y), ∀b ∈ B)}

(1)

WhereB ∈ C is a set of attributes. Granules x and y satisfying the relation I are indiscernible by attributes from B. An ordered pair AS = (U, I) is called a Pawlak approximation space. According to I, we can define two crisp sets B Lower approximation and B upper approximation we call the lower and upper approximation of the set of granules X in the approximation space AS: B Lower = x ∈ U ∶ IB x ⊆ X

(2)

Bupper = {x ∈ U ∶ IB(x) ∩ X ≠ ϕ}

(3)

The decision Table is called consistent when a functional dependency between the set of condition attributes and decision attributes is fulfilled; otherwise the Table is called inconsistent. That is, the Table is inconsistent when it contains rows, which for equal values of condition attributesC, there are different values of decisionD. It is possible to determine the range of consistency in data as [24]: ConsistB X =

J

Granules of knowledge

The lower approximation

B lower x i U

(4) The set of objects

The boundary region

The upper approximation

Fig. 1. Definitions of Approximations Expressed in Terms of Granules of Knowledge.

One of the problems related to the practical application of rough set methods is whether the whole set of attributes is necessary and if not, how to determine the simplified and still sufficient subset of attributes equivalent to the original. The rejected attributes are redundant since their removal cannot worsen the classification. There are usually several such

subsets of attributes and those, which are minimal with respect to inclusion, and are called reduct. Decision rules can be perceived as data patterns, which represent the relationship between attribute values in the classification system. A decision rule is an expression in the formC → D, read “if C then D”, where C and D are logical formulas called condition and decision of the rule, respectively [25]. Let C denote the set of all granules from the universe U, having the property C. If C → D is a decision rule then supp(C; D) =card (|C^D|) and will be called the support of the decision rule and will be referred to as the strength of the decision rule. σ C; D =

supp(C ; D) Card (U)

(5)

With every C → D decision rule we associate a certainty factor that is interpreted as the frequency of granules having the property D in the set of granules and having the property C Cer C; D =

supp(C ; D) Card (|C|)

(6)

And a coverage factor that is interpreted as the frequency of granules containing the property C in the set of granules having the property D. Cov C; D =

supp(C ; D) Card (|D|)

(7)

IV. ROUGH BASED GRANULAR APPROACH ON HCV DATASET Predicting the outcome of a disease is one of the most interesting and challenging tasks. Because a huge volume of medical data became available to the medical research community, new research avenues such as knowledge discovery in databases (KDD), which includes data mining techniques, has become a popular research tool for medical researchers who seek identifying and exploiting patterns and relationships among large number of variables, and to be able to predict the outcome of a disease using the historical cases stored within datasets [26]. The proposed approach uses rough set to identify the most relevant attributes and to induce decision rules from a real life HCV dataset. Information granulation using clustering is also investigated. There are five phases used in Rough based Granular approach (RGA) , as shown in Figure2: (a)Construction of information Table and calculate upper and lower approximation space, (b) Discretization of continued attributes, (c)Calculate dynamic reduction, (d) Rule generation, and (e) Classification of unseen cases. a. Information Table An information Table provides a convenient way to describe a finite set of objects, called a universe, using a finite set of attributes. It represents all available information and knowledge. An information Table is the following: S = (U At, Va a ∈ At , Ia (x) | x ∈ U, a ∈ At})

(8)

Where𝑈 is a finite nonempty set of objects, 𝐴𝑡 is a finite nonempty set of attributes, va is a nonempty set of values of 𝑎 ∈ 𝐴𝑡 , 𝐼𝑎 ∶ 𝑈 → 𝑉 is an information function. The mapping Ia (x) = 𝑣 means that the value of object 𝑥 on attribute 𝑎 is 𝑣, where a 𝑣 ∈ 𝑉. A decision logic language 𝐿 defines the

information in an information Table𝑆. An atomic formula is given by 𝑎 = 𝑣, where 𝑎 ∈ 𝐴𝑡 and 𝑣 ∈ 𝑉𝑎 . If 𝜑and 𝜓are formulas, then so are￢𝜑 , 𝜑 ∧ 𝜓and 𝜑 ∨ 𝜓. Given a formula 𝜑, if an object 𝑥 satisfies 𝜑 , write 𝑥 | = 𝜑 . The set 𝑚𝑆 (𝜑) of objects, defined by, 𝑚𝑆 𝜑 = { 𝑥 ∈ 𝑈 |𝑥| = 𝜑} is called the meaning of the formula 𝜑in 𝑆. If 𝑆 is understood, we simply write 𝑚(𝜑)[27]. For classification tasks, it is assumed that each object in an information Table is associated with a unique class label. Objects can be divided into classes, which form a granulation of the universe. Without loss of generality, we assume that there is a unique attribute class taking class labels as its values. The set of attributes is expressed as 𝐴𝑡 = 𝐷 ∪ {𝑐𝑙𝑎𝑠𝑠}, where 𝐷 is the set of attributes used to describe the objects, also called the set of descriptive attributes. A granule X is a definable granule if it is associated with at least one formula, i.e. X = m φ , whereφ ∈ L . After construct HCV information Table and using an indescribability relation to find the concept granules, rough sets upper and lower approximations are calculated. In Rough based Granular, these approximations consist of several crisp granules (i.e. the granules used are disjoint). The purpose of Granular rough is to seek an approximation scheme which can effectively solve a complex problem, and this guarantees HCV Data Set splitting data set to training

Input The Training informaion Table

Pre-proccesing HCV dataset Discretization using RSBR algorithm

Data Reduction Dynamic Reduction

Reduced Attributes Set

Rule Generation Rule Induction

Decision Rule Selection

Classifcation of HCV Using generated rules from HCV training set to classify HCV test dataset Fig. 2. Rough based Granular Approach (RGA) Used in HCV Dataset Classification

b. Discretization based on RSBR algorithm Discretization means that a notion of “distance” between attribute values is not needed in contrast to many other machine learning techniques. The goal of discretization is to find a set of cut points to partition the range into a small number of intervals that have good class coherence, which is usually measured by an evaluation function. In addition to the maximization of interdependence between class labels and

attribute values, an ideal discretization method should have a secondary goal to minimize the number of intervals without significant loss of class attribute mutual dependence [28]. Algorithm 1.:RSBR Discretization algorithm Input Information system Table (𝑺) with real valued attributes 𝑨𝒊𝒋 And 𝒏 is the number of inter values for each attribute . Output Information Table (𝑺𝑻) with descretized real valued attribute . Begin procedure (1) (2)

For 𝑨𝒊𝒋 ∈ 𝑺 do Define a set of Boolean variables as follows: 𝑛

𝐵𝑉 𝑈 = {

𝑛

𝐶𝑎𝑖 , 𝑖=1

𝑛

𝐶𝑏𝑖 , 𝑖=1

𝑛

𝐶𝑐𝑖 , … . 𝑖=1

𝐶𝑛𝑖 } 𝑖=1

Where 𝑛𝑖=1 𝐶𝑎𝑖 corresponds to a set of intervals Defined on the variables of attributes a. (3) (4)

End for Create new information TableT P by using set of intervals

(5)

Find minimal subset of 𝐶𝑎𝑖 that discerns all the objects in decision class D using the following formula : ΦU =∧{ψ i,j :d xi ≠d(xj )}

Where ψ i,j is the number of minimal cut which must be used to discern two different instances xi and xj in the information Table . End procedure Fig. 3. RSBR Discretization Algorithm

In this work discretization algorithm called RSBR that is based on hybridization of rough sets and Boolean reasoning proposed in [29] is used to discretize the HCV continuous data. The basic concepts of the discretization based on the RSBR can be summarized as shown in Figure 3: (i) discretization of a decision Table, where Vc = [vc ;wc ) is an interval of real values taken by attribute c; is a searching process for a partition P c of Vc for any c ∈ C satisfying some optimization criteria (like a minimal partition) while preserving some discernibility constraints [29]; (ii) any partition of Vc is defined by a sequence of the so-called cuts v1< v2 < …. < vk from Vc; (iii) any family of partitions{P c }c∈C can be identified with a set of cuts. c. Data Reduction Concept This concept is very useful in large datasets. It can lead to better performance and also provide the ability to accommodate noisy data. There are many advantages in using this concept. It reduces redundant attributes, simplifies the structure of an information system, reduces the cost of

instance classification, speeds up the process of rule induction, and even improves the performance of the generated rule systems. Dynamic Reducts algorithm [30]is used to generate reducts, for the problems of standard rough set reducts or static reducts.Dynamic reducts can put up better performance in very large dataset, and also effectively enhances the ability to accommodate noise data. The reducts in a decision system are not stable, but are sensitive for a sample data. Bazan [31] gives the concept and method about dynamic reduct, which grounds the most stable reduct of the decision system in theory. The rules are calculated by means of dynamic reducts and are better predisposed to classify unseen objects, because these reducts are in some sense the most stable reducts. They are the most frequently appearing reducts in the sub-decision system created by random samples of a given decision system. d. Rule Generation Discover the decision rules of our information system, so that we could base our decision on these rules to infer the decision attribute value of an entity due to some conditional attributes value samples of a given decision system. [31]. with every C → D decision rule associate a certainty factor that is interpreted as the frequency of objects which have the property D in the set of objects having the property C. And a coverage factor that is interpreted as the frequency of objects having the property C in the set of objects having the property D. We are especially interested in generating rules which cover the largest parts of the universe U. Covering U with more general rules implies smaller size of a rule set. Algorithm 2.: Dynamic Reduct Algorithm Input

DT - Decision Tablewith discretize values Output Dynamic reducts Begin procedure (1) 𝑅←∅ (2) 𝐷𝑇 ← 𝐶𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒 𝑎𝑙𝑙 𝑟𝑒𝑑𝑢𝑐𝑡 (𝐷𝑇) (3) For (j=1;j62 AND 34 AND 80 AND 13 AND 161,13.1 AND Decision(1) S#G#O#T (AST)3([34, *)) AND Seru Ferritin 3([86, *)) AND Ascites 3(no) AND Spleen3(normal) AND HGB 3([13.1, *)) => Decision(1) Sex(m) AND Seru# Bilrubin (SB)3([0.45, *)) AND Seru# Albuin (SA)3([*, 4.2)) AND Ascites 3(no) AND PLT 3([*, 161.000)) => Decision(1) Source(blood) AND Portal vien ( P#V ) 9([12, *)) AND PCR9([*, 23)) => Decision(-1)

5

Sex(m) AND Source(blood) AND Portal vien ( P#V ) 9([12, *)) AND PCR9([*, 23)) => Decision(-1)

1

2

3

Present (1) Absent (-1) Overall %

Predicted Present (1) Absent (-1) 42 0 0 7 100% 100%

Correct % 100% 100% 100%

Present (1) Absent (-1) Overall %

Predicted Present (1) Absent (-1) 34 2 5 8 87.1795% 80%

Correct % 94.4444% 61.5385% 85.7%

HCV Classification Accuracy During Experiments 120

Classification Accuracy

Card (|C|) supp (C ; D)

Correct % 100% 100% 100%

TABLE VI. HCV CLASSIFICATION TABLE AFTER 9 MONTHS

Card (U) supp (C ; D)

(14) compute rule coverage Cov C; D =

Present (1) Absent (-1) Overall %

Predicted Absent (-1) Present (1) 47 0 0 2 100% 100%

TABLE V. HCV CLASSIFICATION TABLE AFTER 6 MONTHS

Set of decision rules with support ,strength and coverage factors

(13) compute rule certainty Cer C; D =

Sex(m) AND Seru# Bilrubin (SB)3([0.45, *)) AND Ascites 3(no) AND Spleen3(normal) AND PLT 3([*, 161.000)) => Decision(1) Sex(m) AND Ascites 9(no) AND PCR9([*, 23)) => Decision(-1)

TABLE IV. HCV CLASSIFICATION TABLE AFTER 3 MONTHS

Actual

Begin procedure (1) for each correspondence object 𝑥do (2) 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑡ℎ𝑒𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝑟𝑢𝑙𝑒 (𝑐1 = 𝑣1 ∧ 𝑐2 = 𝑣2 ∧. . . .∧ 𝑐𝑛 = 𝑣𝑛) −→ 𝑑 = 𝑢 (3) Scan the reduct 𝑟over an object 𝑥 (4) 𝐶𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡 (𝑐𝑖, 1 ≤ 𝑖 ≤ 𝑛) (5) for every 𝑐 ∈ 𝐶do (6) 𝐴𝑠𝑠𝑖𝑔𝑛𝑡ℎ𝑒𝑣𝑎𝑙𝑢𝑒𝑣𝑡𝑜𝑡ℎ𝑒𝑐𝑜𝑟𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑛𝑐𝑒𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑎 (7) end for (8) 𝐶𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑎𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑑 (9) Assign the value 𝑢to the correspondence decision attribute 𝑑 (10) end for (11) for each 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛𝑟𝑢𝑙𝑒 (𝑐1 = 𝑣1 ∧ 𝑐2 = 𝑣2 ∧. . . .∧ 𝑐𝑛 = 𝑣𝑛) −→ 𝑑 supp (C ; D) (12) compute rule strength σ C; D =

Sex(m) AND Ascites 9(no) AND Portal vien ( P#V ) 9([12, *)) AND PCR9([*, 23)) => Decision(-1)

100

80

60

40

20

0

R1

R2

R3

R4

R5

R6

R7

3 Months 95.9

100

100

100

95.9

100

100

6 Months 77.6

100

100

100

73.5

100

100

9 Months 67.3

67.3

75.5

42.8

67.3

85.7

79.5

Fig. 6. Classifications Accuracy for Each Reduct During Experiment

VI. CONCLUSIONS AND FUTURE WORKS In this paper, a Rough- Granular approach is introduced to classify the effects of a new medication for HCV treatment through the Rough based Granular computing approach which has been used to predict response to new medications for HCV treatment in patients with hepatitis C virus (HCV). The

Rough set technique had been used to discover the dependency among the attributes, and to generate a set of reducts which consist of a minimal number of attributes and generate reduced sets of decision rules with the same significance as the whole sets depending on its concept of dynamic reduct. Based on the rough set concept of rule support, the most promising rule is discovered and chosen with highest priority with high classification accuracy. Although, Hybrid models achieve good classification accuracy there are some limitations like data reduction processing time .Therefore, in future progress, we hope to apply new hybrid approaches with larger datasets and compare them with the proposed approach and other techniques to reach as high accuracy as possible. ACKNOWLEDGMENT Authors would like to thank Prof. Samia A. Hawas, Prof. of Immunology, Faculty of Medicine, Mansoura University, Mansoura, Egypt for her great support during clinical study. REFERENCES [1] Booth, J., O Grady, J., Neuberger, J.: Clinical guidelines on the management of hepatitis C, vol. 1, pp. 11–21 (2001) [2] Huda Yasin, Tahseen A. Jilani ,Madiha Danish,” Hepatitis-C Classification using Data Mining Techniques” International Journal of Computer Applications (0975 – 8887) ,Volume 24– No.3, June 2011. [3] Kedziora, P., Figlerowicz, M., Formanowicz, P., Alejska, M., Jackowiak, P., Mali-nowska, N., Fratczak, A., Blazewicz, J., Figlerowicz, M.: Computational Methods in Diagnostics of Chronic Hepatitis C. Bulletin of the Polish Academy of Sciences, Technical Sciences 53(3), 273–281 (2005) [4]Karen F. Murray and Robert L. Carithers, Jr.: AASLD Practice Guidelines: Evaluation of the Patient for Liver Transplantation. Image and Vision Computing 24,(2006). [5] Hodgson, S., Harrison, R.F., Cross, S.S.: An automated Pattern recognition system for the quantification of Inflammatory cells in hepatitis-C-infected liver biopsies.Image and Vision Computing 24, 1025–1038 (2006). [6] Ahmed mohamed samir ali gamal eldin ,” A Data Mining Approach for the Prediction of Hepatitis C Virus protease Cleavage Sites”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12, December 2011. [7]Ola G. Wasel, Adel F. Badria,Abdelaziz A. Mohamed, Mona K.Marei , “Hepatogenic Differentiation of Rabbit BM-MSC using Noncoated Flask”, Electronic Journal of Biology, Vol. 9(1):1-7,2013. [8]Don C. Rockey,1Stephen H. Caldwell,2Zachary D. Goodman,3Rendon C. Nelson,4and Alastair D. Smith5,” Liver Biopsy”,(2006). [9] Ishak, K., Baptista, A., Histological, L.B..” Histological grading and staging of chronic hepatitis”, Journal of Hepatology 22, 696–699 (1995). [10] Szymon, W.,"Towards prediction of HCV therapy efficiency”, Computational and Mathematical Methods in Medicine,185-199,11(2) 2010. [11] Asselah, T. , "Hepatitis C: viral and host factors associated with non-response to pegylated interferon plus ribavirin", Liver International, , pp. 1259-1269,30, 2010. [12] Berenguer M., “A Model to Predict Severe HCV-Related Disease Following Live Transplantation”, HEPATOLOGY, Vol. 38, No. 1, PP. 34-41,2003. [13] Moucari R.,“High predictive value of early viral kinetics in retreatment with peg interferon and ribavirin of chronic hepatitis C patients non-responders to standard combination therapy”, Journal of Hepatology 46,, PP. 596–604, 2007. [14] Wang, D., et al., "A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy," Artificial Intelligence in Medicine, 47, 63-74, 2009.

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