Detection of Anemia using Fuzzy Logic
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This document presents a fuzzy logic approach for detecting anemia using clinical test results. It describes developing a fuzzy expert system with 3 input variables (hemoglobin, mean corpuscular volume, mean corpuscular hemoglobin concentration) and 1 output variable (type of anemia). Fuzzy sets and rules are defined to classify anemia based on the input clinical values. The system was tested on sample input values and correctly classified the type of anemia based on the fuzzy logic rules. The approach aims to help doctors more accurately detect anemia using a fuzzy expert system compared to probabilistic logic or relying solely on symptoms.
![International Journal of Computer Applications Technology and Research
Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656
www.ijcat.com 762
Detection of Anemia using Fuzzy Logic
Sonu Malu
Mewar University
Chittorgarh, Rajasthan
India
B. L. Pal
Mewar University
Chittorgarh, Rajasthan
India
Shiv Kumar
Mewar University
Chittorgarh, Rajasthan
India
Abstract: Medical Science is considered as a field of uncertainty, vagueness and complexity. Fuzzy logic plays an important role to
deal with these uncertainty, vagueness and complexity. Detection of diseases in medical is a very difficult task. To improve accuracy
rate engineers helping in detection of the diseases by developing the Expert System using Fuzzy Logic. Fuzzy logic consists of many
valued logic. It has varying values in the range of 0 and 1 instead of fix values. In this study, we developed a Fuzzy Expert system to
detect Anemia on the basis of Symptoms as well as clinical test.
Keywords: Anemia, Fuzzy Logic, Fuzzy Expert System, CBC Test
1. INTRODUCTION
The blood that circulates throughout the body performs a number
of critical functions. It delivers oxygen, removes carbon dioxide
CO2, and carries life sustaining nutrition’s. By acting as the
vehicle for long-distance messengers such as hormones, blood
helps the various parts of the body communicate with each other.
This is carried out by blood cells through working in partnership
with the liquid part of the blood (plasma). Anemia is a condition
where number of healthy RBC in the blood is lower than normal.
It is due to low RBC’s, destruction of RBC’s or loses of too
many RBC’s. If your blood does not have enough RBC’s, your
body doesn’t get enough oxygen it needs. As a result you may
feel tired and other symptoms. But sometimes it is very difficult
to detect anemia on the basis of symptoms only. In the domain of
Anemia there is no such boundary between what is healthy and
what is diseased. Having so many factors to detect anemia makes
doctor’s work difficult. So, Experts require an accurate tool that
considering these risk factors and give some certain result for
uncertain terms.
2. LITERATURE REVIEW
When the studies in the literature related with this classification
application are examined, it can be seen that a great variety of
methods were used. Among these, [5] Fuzzy System have been
used to diagnose the different types of anemia on the basis of
symptoms such as Irritability, tachycardia, Memory weakness,
Bleeding and Chronic fatigue. Another, [6] diagnose Liver
disease using fuzzy logic on the basis of CBC Test which uses 4
parameters such as WBC, HGB, HCT and PLT. [7] Ali.Adeli,
Mehdi. Neshat proposed a system to diagnose the heart disease
using fuzzy logic. [8] Nidhi Mishra and Dr. P Jha also develop
a fuzzy expert system to diagnose the Sickle Cell Anemia.
3. OBJECTIVES
The Objectives are:
1. Detect Anemia using Fuzzy Logic.
2. Classify Anemia on the basis of Accuracy.
4. DESIGN MODEL
4.1 Introduction
Three steps are used to monitor general health and Anemia. But
we are focusing only on the Tests and Procedures. Three steps
are as follows:
1. Medical and Family Histories
2. Physical Exam
3. Tests and Procedures.
4.2 Design of Fuzzy Logic System
Design model divided into five steps:
1. Problem Specification & define linguistic variables.
2. Define Fuzzy sets.
3. Define Fuzzy Rule.
4. Encode Fuzzy Sets, Fuzzy Rules and Procedures to
build Expert System.
5. METHOD
we describe the designing of the fuzzy expert system.
5.1 Design a Fuzzy Logic System
5.1.1 Problem Specification & Define linguistic
variables: There are 3 input variables and 1 output variables.
Linguistic Variables:
For Input Variables](https://image.slidesharecdn.com/ijcatr04101008-151114082102-lva1-app6891/85/Detection-of-Anemia-using-Fuzzy-Logic-1-320.jpg)
![International Journal of Computer Applications Technology and Research
Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656
www.ijcat.com 763
Table 1 Linguistic Variables for Input Variables
S.No. Input Variables Linguistic
Variables
1 Hemoglobin HGB
2 Mean Corpuscular
Volume
MCV
3 Mean Corpuscular
Hemoglobin
Concentration
MCHC
For Output Variables
Table 2 Linguistic Variables for Output Variables
5.1.2 Define Fuzzy Sets:
Input Variables & Value Ranges:
Table 3 Values for all Input Linguistic Variables[6]
S.No.
Linguistic
Variable
Ranges Values
1 HGB
5 - 13.8
grams/deciliter
Low
13.9 to 16.3
grams/deciliter
Medium
16.4 – 18
grams/deciliter
High
2 MCV
60 – 79.9 fl Low
79.9 to 100 fl Medium
100.1 - 120 fl High
3 MCHC
28 - 31.9
grams/deciliter
Low
32 - 36
grams/deciliter
Medium
36.1 - 40
grams/deciliter
High
Output Variables & Value Ranges:
Table 4 Values for all Output Linguistic
Variables[6].
S.No. Linguistic
Variable
Ranges Values
1.
Types of
Anemia
HGB is 5 –
13.8 g/dl
MicroCytic
HypoChro
mic
MCV is 60
– 79.9 fl
MCHC is
28 – 31.9
g/dl
2.
HGB is 5 –
13.8 g/dl
MicroCytic
NormoChr
omic
MCV is 60
– 79.9 fl
MCHC is
32 - 36g/dl
3.
HGB is 5 –
13.8 g/dl
MicroCytic
HyperChro
mic
MCV is 60
– 79.9 fl
MCHC is
36.1 - 40
g/dl
4 HGB is 5 –
13.8 g/dl NormoCyti
c
HypoChro
mic
MCV is 80
- 100 fl
MCHC is
S.No Output Variables Linguistic
Variables
1 Anemia Types of Anemia](https://image.slidesharecdn.com/ijcatr04101008-151114082102-lva1-app6891/85/Detection-of-Anemia-using-Fuzzy-Logic-2-320.jpg)
![International Journal of Computer Applications Technology and Research
Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656
www.ijcat.com 764
28 – 31.9
g/dl
5 HGB is 5 –
13.8 g/dl
NormoCyti
c
NormoCHr
omic
MCV is 80
- 100 fl
MCHC is
32 - 36
g/dl
6 HGB is 5 –
13.8 g/dl
NormoCyti
c
HyperChro
mic
MCV is 80
- 100 fl
MCHC is
36.1 – 40
g/dl
7 HGB is 5 –
13.8 g/dl
MacroCytic
HypoChro
mic
MCV is
100.1 –
120 fl
MCHC is
28 – 31.9
g/dl
8 HGB is 5 –
13.8 g/dl
MacroCytic
NormoCHr
omic
MCV is
100.1 –
120 fl
MCHC is
32 - 36g/dl
9 HGB is 5 –
13.8 g/dl
MacroCytic
HyperChro
mic
MCV is
100.1 –
120 fl
MCHC is
36.1 – 40
g/dl
5.1.3 Define Fuzzy Rules:
As we have total 3 input variables so total number of possible
non conflicting fuzzy inference rules are 32
= 9 rules.
First 3 rules are for Symptoms based testing:
1. If ( irritation is Effective ) && ( Heart_Rate is High ) && (
Disorder is cancer ) then HGB is low.
2. If ( irritation is Effective ) && ( Heart_Rate is High ) &&(
Disorder is cancer ) && ( Blood_Loss is Stomach / intestine
bleeding ) then HGB is low.
3. If ( irritation is Effective ) && ( Heart_Rate is High ) && (
Disorder is cancer ) && ( Blood_Loss is Stomach / intestine
bleeding ) && ( Weak_Memory is Effective ) then HGB is
low[5].
Further, 3 rules are for the classification of anemia on the
basis of MCV only:
4. If ( HGB is Low ) && ( MCV is Low ) then MicroCytic is
High.
5. If ( HGB is Low ) && ( MCV is Medium) then NormoCytic
is high.
6. If ( HGB is Low ) && ( MCV is High ) then MacroCytic is
high.
At last 9 rules are for the further classification of anemia on
the basis of all three parameters such as HGB, MCV, &
MCHC.
7. If ( HGB is Low ) && ( MCV is Low ) && ( MCHC is Low
) then MicroCytic is HypoChromic.
8. If ( HGB is Low ) && ( MCV is Low ) && ( MCHC is
Medium ) then MicroCytic is NormoChromic.
9. If ( HGB is Low ) && (MCV is Low ) && ( MCHC is High
) then MicroCytic is HyperChromic.
10. If ( HGB is Low ) && ( MCV is Medium ) && (
MCHC is Low ) then NormoCytic is HypoChromic.
11. If ( HGB is Low ) && ( MCV is Medium ) && ( MCHC is
Medium ) then NormoCytic is NormoChromic.
12. If ( HGB is Low ) && ( MCV is Medium ) && ( MCHC is
High ) then NormoCytic is HyperChromic.
13. If ( HGB is Low ) && ( MCV is High ) && ( MCHC is
Low ) then MacroCytic is HypoChromic.
14. If ( HGB is Low ) && ( MCV is High ) && ( MCHC is
Medium ) then MacroCytic is NormoChromic.
15. If ( HGB is Low ) && ( MCV is High ) && ( MCHC is
High ) then MacroCytic is HyperChromic[6].
Table 5 Illustration of applied rules with Respect to MF[6]
Rule
No.
Linguist
ic
Variable
1
Linguis
tic
Variabl
e2
Linguist
ic
Variable
3
Result](https://image.slidesharecdn.com/ijcatr04101008-151114082102-lva1-app6891/85/Detection-of-Anemia-using-Fuzzy-Logic-3-320.jpg)
![International Journal of Computer Applications Technology and Research
Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656
www.ijcat.com 766
past time expert system are more efficient and less expensive. It
detect anemia on the basis of both Symptoms and CBC Test.
From the viewpoint of an end-user, the results of this work can
facilitate laboratory work by reducing the time and cost.
8. FUTURE WORK
The future work will focus on developing a machine learning
approach to classify different types of anemic RBCs in
microscopic images. The method described in this dissertation
can be extends in future very efficiently. We can classify
anemia on the basis of RBC structure using digital image
processing. We can also provide some CBC reports and load
that report as it is in our system and detect anemia in future. We
can also detect anemia and classify it only on the the basis of
CBC Test without using symptoms test.
9. ACKNOWLDGEMENT
I wish to express my deep sense of gratitude to my dissertation
guide Mr. B.L. Pal, Assistant Professor, Computer science and
Engineering department, Mewar University, for his excellence
guidance, valuable suggestion that greatly helped me to
complete the dissertation successfully. I would like to place on
record my deep sense of gratitude to Mr. Shiv Kumar,
Assistant Professor, Department of Computer Science &
Engineering, Mewar University, for his stimulating co-
operation, unfailing inspiration and continuous encouragement
throughout the course of present work.
10. REFERENCES
[1] Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George,
Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy,
“Comparative Study Using Weka for Red Blood Cells
Classification”, World Academy of Science, Engineering and
Technology International Journal of Medical, Health,
Biomedical and Pharmaceutical Engineering Vol:9, No:1, 2015.
[2] Hematology: The study of Blood.
[3] Yared Alemu, Alemayehu Atomsa, and Zewdneh
Sahlemariam, “Hematology”, Jimma University, 2006.
[4] National Heart, Lung and Blood Institute, U.S. Department
of Health and Human Services, publication no. 11-7629A,
September-2011.
[5] Javad Aramideh and Hamed Jelodar, Application of fuzzy
logic for presentation of an expert fuzzy system to diagnose
Anemia, Indian Journal of science and technology, Vol. 7(7),
933-938, July 2014.
[6] Asma Hashmia, Dr. Muhammad Saleem Khanb, Diagnosis
Blood Test for Liver Disease using Fuzzy Logic, International
Journal of Sciences : Basic and Applied Research, Volume 20,
No 1, pp 151-183, 2015.
[7] N.Tamil Selvi, S. Saranya, P. Usha, M. Yashodha,
Perception of Tired Blood and its causes using mining
Techniques, International Journal of Engineering Sciences &
Research Technology, ISSN: 2277-9655, Vol 4(1), January-
2015.
[8] P. Usha, S.Saranya and N. Tamilselvi, Prevalence and risk
factors of anemia along with classifiers, International Journal of
Scientific & Engineering Research, Vol. 4, Issue 5, May-2013.
[9] Aqueel Ahmed and Shaikh Abdul Hannan, Data Mining
Techniques to find out heart diseases : An Overview,
International Journal of Innovative Technology and Exploring
Engineering, ISSN: 2278-3075, Vol. 1, Issue 4, September-
2012.
[10] Eva C. Guinan, Diagnosis and management of Aplastic
Anemia, American Society of Hematology, 2011.
[11] G. Licata, Employing fuzzy logic in the diagnosis pf
clinical case, Health Journal, Vol. 2, No. 3, 211-224 , 2010.
[12] Mayo Clinic : http://www.mayoclinic.org](https://image.slidesharecdn.com/ijcatr04101008-151114082102-lva1-app6891/85/Detection-of-Anemia-using-Fuzzy-Logic-5-320.jpg)
Detection of Anemia using Fuzzy Logic
- 1. International Journal of Computer Applications Technology and Research Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656 www.ijcat.com 762 Detection of Anemia using Fuzzy Logic Sonu Malu Mewar University Chittorgarh, Rajasthan India B. L. Pal Mewar University Chittorgarh, Rajasthan India Shiv Kumar Mewar University Chittorgarh, Rajasthan India Abstract: Medical Science is considered as a field of uncertainty, vagueness and complexity. Fuzzy logic plays an important role to deal with these uncertainty, vagueness and complexity. Detection of diseases in medical is a very difficult task. To improve accuracy rate engineers helping in detection of the diseases by developing the Expert System using Fuzzy Logic. Fuzzy logic consists of many valued logic. It has varying values in the range of 0 and 1 instead of fix values. In this study, we developed a Fuzzy Expert system to detect Anemia on the basis of Symptoms as well as clinical test. Keywords: Anemia, Fuzzy Logic, Fuzzy Expert System, CBC Test 1. INTRODUCTION The blood that circulates throughout the body performs a number of critical functions. It delivers oxygen, removes carbon dioxide CO2, and carries life sustaining nutrition’s. By acting as the vehicle for long-distance messengers such as hormones, blood helps the various parts of the body communicate with each other. This is carried out by blood cells through working in partnership with the liquid part of the blood (plasma). Anemia is a condition where number of healthy RBC in the blood is lower than normal. It is due to low RBC’s, destruction of RBC’s or loses of too many RBC’s. If your blood does not have enough RBC’s, your body doesn’t get enough oxygen it needs. As a result you may feel tired and other symptoms. But sometimes it is very difficult to detect anemia on the basis of symptoms only. In the domain of Anemia there is no such boundary between what is healthy and what is diseased. Having so many factors to detect anemia makes doctor’s work difficult. So, Experts require an accurate tool that considering these risk factors and give some certain result for uncertain terms. 2. LITERATURE REVIEW When the studies in the literature related with this classification application are examined, it can be seen that a great variety of methods were used. Among these, [5] Fuzzy System have been used to diagnose the different types of anemia on the basis of symptoms such as Irritability, tachycardia, Memory weakness, Bleeding and Chronic fatigue. Another, [6] diagnose Liver disease using fuzzy logic on the basis of CBC Test which uses 4 parameters such as WBC, HGB, HCT and PLT. [7] Ali.Adeli, Mehdi. Neshat proposed a system to diagnose the heart disease using fuzzy logic. [8] Nidhi Mishra and Dr. P Jha also develop a fuzzy expert system to diagnose the Sickle Cell Anemia. 3. OBJECTIVES The Objectives are: 1. Detect Anemia using Fuzzy Logic. 2. Classify Anemia on the basis of Accuracy. 4. DESIGN MODEL 4.1 Introduction Three steps are used to monitor general health and Anemia. But we are focusing only on the Tests and Procedures. Three steps are as follows: 1. Medical and Family Histories 2. Physical Exam 3. Tests and Procedures. 4.2 Design of Fuzzy Logic System Design model divided into five steps: 1. Problem Specification & define linguistic variables. 2. Define Fuzzy sets. 3. Define Fuzzy Rule. 4. Encode Fuzzy Sets, Fuzzy Rules and Procedures to build Expert System. 5. METHOD we describe the designing of the fuzzy expert system. 5.1 Design a Fuzzy Logic System 5.1.1 Problem Specification & Define linguistic variables: There are 3 input variables and 1 output variables. Linguistic Variables: For Input Variables
- 2. International Journal of Computer Applications Technology and Research Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656 www.ijcat.com 763 Table 1 Linguistic Variables for Input Variables S.No. Input Variables Linguistic Variables 1 Hemoglobin HGB 2 Mean Corpuscular Volume MCV 3 Mean Corpuscular Hemoglobin Concentration MCHC For Output Variables Table 2 Linguistic Variables for Output Variables 5.1.2 Define Fuzzy Sets: Input Variables & Value Ranges: Table 3 Values for all Input Linguistic Variables[6] S.No. Linguistic Variable Ranges Values 1 HGB 5 - 13.8 grams/deciliter Low 13.9 to 16.3 grams/deciliter Medium 16.4 – 18 grams/deciliter High 2 MCV 60 – 79.9 fl Low 79.9 to 100 fl Medium 100.1 - 120 fl High 3 MCHC 28 - 31.9 grams/deciliter Low 32 - 36 grams/deciliter Medium 36.1 - 40 grams/deciliter High Output Variables & Value Ranges: Table 4 Values for all Output Linguistic Variables[6]. S.No. Linguistic Variable Ranges Values 1. Types of Anemia HGB is 5 – 13.8 g/dl MicroCytic HypoChro mic MCV is 60 – 79.9 fl MCHC is 28 – 31.9 g/dl 2. HGB is 5 – 13.8 g/dl MicroCytic NormoChr omic MCV is 60 – 79.9 fl MCHC is 32 - 36g/dl 3. HGB is 5 – 13.8 g/dl MicroCytic HyperChro mic MCV is 60 – 79.9 fl MCHC is 36.1 - 40 g/dl 4 HGB is 5 – 13.8 g/dl NormoCyti c HypoChro mic MCV is 80 - 100 fl MCHC is S.No Output Variables Linguistic Variables 1 Anemia Types of Anemia
- 3. International Journal of Computer Applications Technology and Research Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656 www.ijcat.com 764 28 – 31.9 g/dl 5 HGB is 5 – 13.8 g/dl NormoCyti c NormoCHr omic MCV is 80 - 100 fl MCHC is 32 - 36 g/dl 6 HGB is 5 – 13.8 g/dl NormoCyti c HyperChro mic MCV is 80 - 100 fl MCHC is 36.1 – 40 g/dl 7 HGB is 5 – 13.8 g/dl MacroCytic HypoChro mic MCV is 100.1 – 120 fl MCHC is 28 – 31.9 g/dl 8 HGB is 5 – 13.8 g/dl MacroCytic NormoCHr omic MCV is 100.1 – 120 fl MCHC is 32 - 36g/dl 9 HGB is 5 – 13.8 g/dl MacroCytic HyperChro mic MCV is 100.1 – 120 fl MCHC is 36.1 – 40 g/dl 5.1.3 Define Fuzzy Rules: As we have total 3 input variables so total number of possible non conflicting fuzzy inference rules are 32 = 9 rules. First 3 rules are for Symptoms based testing: 1. If ( irritation is Effective ) && ( Heart_Rate is High ) && ( Disorder is cancer ) then HGB is low. 2. If ( irritation is Effective ) && ( Heart_Rate is High ) &&( Disorder is cancer ) && ( Blood_Loss is Stomach / intestine bleeding ) then HGB is low. 3. If ( irritation is Effective ) && ( Heart_Rate is High ) && ( Disorder is cancer ) && ( Blood_Loss is Stomach / intestine bleeding ) && ( Weak_Memory is Effective ) then HGB is low[5]. Further, 3 rules are for the classification of anemia on the basis of MCV only: 4. If ( HGB is Low ) && ( MCV is Low ) then MicroCytic is High. 5. If ( HGB is Low ) && ( MCV is Medium) then NormoCytic is high. 6. If ( HGB is Low ) && ( MCV is High ) then MacroCytic is high. At last 9 rules are for the further classification of anemia on the basis of all three parameters such as HGB, MCV, & MCHC. 7. If ( HGB is Low ) && ( MCV is Low ) && ( MCHC is Low ) then MicroCytic is HypoChromic. 8. If ( HGB is Low ) && ( MCV is Low ) && ( MCHC is Medium ) then MicroCytic is NormoChromic. 9. If ( HGB is Low ) && (MCV is Low ) && ( MCHC is High ) then MicroCytic is HyperChromic. 10. If ( HGB is Low ) && ( MCV is Medium ) && ( MCHC is Low ) then NormoCytic is HypoChromic. 11. If ( HGB is Low ) && ( MCV is Medium ) && ( MCHC is Medium ) then NormoCytic is NormoChromic. 12. If ( HGB is Low ) && ( MCV is Medium ) && ( MCHC is High ) then NormoCytic is HyperChromic. 13. If ( HGB is Low ) && ( MCV is High ) && ( MCHC is Low ) then MacroCytic is HypoChromic. 14. If ( HGB is Low ) && ( MCV is High ) && ( MCHC is Medium ) then MacroCytic is NormoChromic. 15. If ( HGB is Low ) && ( MCV is High ) && ( MCHC is High ) then MacroCytic is HyperChromic[6]. Table 5 Illustration of applied rules with Respect to MF[6] Rule No. Linguist ic Variable 1 Linguis tic Variabl e2 Linguist ic Variable 3 Result
- 4. International Journal of Computer Applications Technology and Research Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656 www.ijcat.com 765 (HGB) (MCV) (MCHC) 1 Low Low Low MicroCy tic is HypoCH romic 2 Low Low Medium MicroCy tic is NormoC hromic 3 Low Low High MicroCy tic is HyperCh romic 4 Low Mediu m Low NormoC ytic is HypoChr omic 5 Low Mediu m Medium NormoC ytic is NormoC hromic 6 Low Mediu m High NormoC ytic is HyperCh romic 7 Low High Low MacroCy tic is HypoChr omic 8 Low High Medium MacroCy tic is NormoC hromic 9 Low High High MacroCy tic is Hyperhr omic 5.1.4 Build Fuzzy Expert System: Form: Figure 1 Input Form 6. RESULTS AND DISCUSSION Table 6 Input Values for Results S. No Input Variable Values Ranges Ranges Selected 1 HGB 10.9 g/dl 5< 10.9 < 18 g/dl 2 MCV 31.00 fl 60< 31.00 < 79.9 fl 3 MCHC 30 g/dl 28< 30 <31.9 g/dl Figure 2 Result from given Input Values 7. CONCLUSION In this paper, fuzzy logic is applied to classify and detect Anemia on the basis of CBC Test. The success of fuzzy detection in its application to a real clinical case shows that fuzzy detection is an improvement of probabilistic logic. Results have been shown from this fuzzy expert system with
- 5. International Journal of Computer Applications Technology and Research Volume 4– Issue 10, 762 - 766, 2015, ISSN: 2319–8656 www.ijcat.com 766 past time expert system are more efficient and less expensive. It detect anemia on the basis of both Symptoms and CBC Test. From the viewpoint of an end-user, the results of this work can facilitate laboratory work by reducing the time and cost. 8. FUTURE WORK The future work will focus on developing a machine learning approach to classify different types of anemic RBCs in microscopic images. The method described in this dissertation can be extends in future very efficiently. We can classify anemia on the basis of RBC structure using digital image processing. We can also provide some CBC reports and load that report as it is in our system and detect anemia in future. We can also detect anemia and classify it only on the the basis of CBC Test without using symptoms test. 9. ACKNOWLDGEMENT I wish to express my deep sense of gratitude to my dissertation guide Mr. B.L. Pal, Assistant Professor, Computer science and Engineering department, Mewar University, for his excellence guidance, valuable suggestion that greatly helped me to complete the dissertation successfully. I would like to place on record my deep sense of gratitude to Mr. Shiv Kumar, Assistant Professor, Department of Computer Science & Engineering, Mewar University, for his stimulating co- operation, unfailing inspiration and continuous encouragement throughout the course of present work. 10. REFERENCES [1] Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy, “Comparative Study Using Weka for Red Blood Cells Classification”, World Academy of Science, Engineering and Technology International Journal of Medical, Health, Biomedical and Pharmaceutical Engineering Vol:9, No:1, 2015. [2] Hematology: The study of Blood. [3] Yared Alemu, Alemayehu Atomsa, and Zewdneh Sahlemariam, “Hematology”, Jimma University, 2006. [4] National Heart, Lung and Blood Institute, U.S. Department of Health and Human Services, publication no. 11-7629A, September-2011. [5] Javad Aramideh and Hamed Jelodar, Application of fuzzy logic for presentation of an expert fuzzy system to diagnose Anemia, Indian Journal of science and technology, Vol. 7(7), 933-938, July 2014. [6] Asma Hashmia, Dr. Muhammad Saleem Khanb, Diagnosis Blood Test for Liver Disease using Fuzzy Logic, International Journal of Sciences : Basic and Applied Research, Volume 20, No 1, pp 151-183, 2015. [7] N.Tamil Selvi, S. Saranya, P. Usha, M. Yashodha, Perception of Tired Blood and its causes using mining Techniques, International Journal of Engineering Sciences & Research Technology, ISSN: 2277-9655, Vol 4(1), January- 2015. [8] P. Usha, S.Saranya and N. Tamilselvi, Prevalence and risk factors of anemia along with classifiers, International Journal of Scientific & Engineering Research, Vol. 4, Issue 5, May-2013. [9] Aqueel Ahmed and Shaikh Abdul Hannan, Data Mining Techniques to find out heart diseases : An Overview, International Journal of Innovative Technology and Exploring Engineering, ISSN: 2278-3075, Vol. 1, Issue 4, September- 2012. [10] Eva C. Guinan, Diagnosis and management of Aplastic Anemia, American Society of Hematology, 2011. [11] G. Licata, Employing fuzzy logic in the diagnosis pf clinical case, Health Journal, Vol. 2, No. 3, 211-224 , 2010. [12] Mayo Clinic : http://www.mayoclinic.org