![IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 13, No. 4, December 2024, pp. 4062~4070
ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i4.pp4062-4070 4062
Journal homepage: http://ijai.iaescore.com
Implementation of fuzzy logic approach for thalassemia
screening in children
Erliyan Redy Susanto1,2
, Admi Syarif1
, Warsito1
, Khairun Nisa Berawi3
,
Putu Ristyaning Ayu Sangging3
, Agus Wantoro2
1
Faculty of Mathematics and Natural Science, Lampung University, Bandar Lampung, Indonesia
2
Department of Information System, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia,
Bandar Lampung, Indonesia
3
Department of Biomolecular, Biochemistry and Physiology, Faculty of Medical, Lampung University, Bandar Lampung, Indonesia
Article Info ABSTRACT
Article history:
Received May 22, 2023
Revised Nov 19, 2023
Accepted Dec 6, 2023
Thalassemia is one of the most dangerous blood disorders that can lead to
severe complications. It is an inherited disease, usually detected after a child
is two to four years old. Identification of thalassemia is a complex task,
involving many variables. Doctors generally diagnose thalassemia by using a
complete blood count (CBC) and high-performance liquid chromatography
(HPLC) test results. However, HPLC tests are expensive and time-
consuming, hence the need for other methods to identify thalassemia. There
are many studies on the application of artificial intelligence for medical
applications. In this study, we developed a new fuzzy-based approach to
identify thalassemia based on a patient’s blood laboratory results. First, we
analyzed the CBC data for blood disorder prediction. Secondly, we adopt the
test results of peripheral blood smear (PBS) to identify whether the person
has thalassemia. We conducted several experiments using 30 (thirty) hospital
patient data and the results were compared with the results provided by
experts. The experimental results show that the system can determine blood
disorders with 93% accuracy and 100% precision in thalassemia prediction.
This system is very effective to help doctors in diagnosing thalassemia
patients.
Keywords:
Artificial intelligence
Blood disorder
Fuzzy approach
Inherited disease
Thalassemia
This is an open access article under the CC BY-SA license.
Corresponding Author:
Admi Syarif
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Lampung University
St. Prof. Dr. Sumantri Brojonegoro No.1, Bandar Lampung, Lampung 35145, Indonesia
Email: admi.syarif@fmipa.unila.ac.id
1. INTRODUCTION
Blood disorders are known to require very expensive medical expenses. Some blood disorders, such
as thalassemia, hemophilia, blood clots, blood cancer, leukemia, lymphoma, and myeloma, require a lot of
money. Diagnosing thalassemia is a challenging task. In addition to cancer, it is considered one of the most
dangerous diseases in the world [1]. Thalassemia is one type of blood disorder due to genetic characteristics
derived from both parents [2], [3]. Another possibility that it can cause is when a gene mutation occurs [4].
This disease is commonly known from infancy [5], [6]. In some cases, thalassemia is discovered after a
person grows up [4]. When treatment is given late, the disease can cause growth delays in the child and other
complications. Thalassemia is also divided into two categories: transfusion-dependent and nontransfusion-
dependent [7]. The easiest way to perform a blood transfusion is by knowing the hemoglobin levels in the
blood [7], [8]. If it is below the standard value, a blood transfusion should be initiated immediately.
Thalassemia patients often experience anemia which can cause fatigue, weakness, and difficulty in](https://image.slidesharecdn.com/3323576-250625093105-e471097b/85/Implementation-of-fuzzy-logic-approach-for-thalassemia-screening-in-children-1-320.jpg)
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performing physical activity. They may also experience delayed growth and abnormal bone development.
Most of these cases are found in children over the age of two years. Thalassemia detection can be done
through a blood test in a laboratory, commonly called screening. Thalassemia screening has several
components of blood tests, including complete blood count (CBC) and high-performance liquid
chromatography (HPLC) [9], [10]. Doctors usually use CBC to predict a blood disorder [11], [12]. Some of
the CBC components that have been used include hemoglobin (HGB), mean corpuscular volume (MCV), and
mean corpuscular hemoglobin (MCH) [13]. We found the problem that HPLC inspection is expensive, and
the consumption time is prolonged [10], [14], [15]. Thus, our study aims to use a new fuzzy-based approach
to minimize costs and accelerate the screening time for thalassemia.
Artificial intelligence (AI) is one of the fields of computer science that is constantly developing and
widely applied in various fields [16]. In multiple studies, AI has been widely used to solve problems related
to medicine, agriculture, and business [17], [18]. It is also used to predict the presence of blood disorders
[18], [19]. A significant problem in AI applications is dealing with non-deterministic data or information.
One popular approach to this issue is to adopt a fuzzy approach. Since Zadeh [20] introduced it, fuzzy has
been widely used for various applications to solve various problems of disease in humans [21], animals [22],
and also plants [23]. A fuzzy approach to predicting thalassemia in children can overcome several challenges
[24]. Fuzzy logic can help analyze interpretations that are incomplete, ambiguous, or subject to interpretation.
This problem makes them suitable for dealing with thalassemia’s complex and diverse clinical picture. The
proposed method uses a fuzzy approach to provide a more accurate and efficient method for predicting
thalassemia in children.
Thalassemia is very important to study and many studies on the use of AI for thalassemia problems
have been conducted. The use of AI has addressed a number of challenges associated with thalassemia. We
have summarized some AI approaches to solve some problems in thalassemia presented in Table 1.
Table 1. AI research on thalassemia
Year Methods Topics Authors
2010 Multi-layer perceptron, k-nearest neighbors,
bayesian networks, naïve-Bayes, and
multinomial logistic regression
The effeciency of data types for classification
performance of machine learning techniques for
screening β-Thalassemia
Paokanta et al. [25]
2016 Fuzzy logic Design of a fuzzy model for thalassemia disease
diagnosis: using mamdani type fuzzy inference
system (FIS)
Thakur et al. [11]
2017 Fuzzy logic Thalassemia risk prediction model using fuzzy
inference systems: an application of fuzzy logic
Thakur and Raw [12]
2017 Fuzzy logic Using fuzzy logic for improving daily clinical
care of β-Thalassemia patients
Santini et al. [23]
2018 Support vector machine Detection of β thalassemia carriers by red cell
parameters obtained from automatic counters
using mathematical formulas
Roth et al. [13]
2019 K-nearest neighbor and naïve Bayes Classification of thalassemia data using k-nearest
neighbor and naïve Bayes
Siswantining et al. [24]
2020 Random forest Classification of thalassemia data using random
forest algorithm
Aszhari et al. [17]
2020 Machine learning and deep learning Artificial intelligence in hematology: current
challenges and opportunities
Radakovich et al. [18]
2021 Ensemble classifiers Classification of β-thalassemia carriers from red
blood cell indices using ensemble classifier
Sadiq et al. [16]
2022 Machine learning and deep learning A review of artificial intelligence applications in
hematology management: current practices and
future prospects
Alaoui et al. [19]
Table 1 shows that various thalassemia problems worked a lot with AI. Some studies on AI with
fuzzy logical approaches have excellent prediction accuracy [23], [26], [27]. Therefore, we use this approach
to solve problems in thalassemia. This article aims to predict blood disorder in a child, focusing on
thalassemia with a fuzzy approach. Our systems require data from laboratory tests such as CBC and
peripheral blood smear (PBS). Thalassemia, referred to in this article, is a beta (β) type. This type of
Thalassemia is most common in Indonesia and other Asian countries. This article perfected previous research
on classifying Thalassemia [11], [28] by adding PBS as a new parameter.
This article consists of two stages. First, the system detects blood disorders by analyzing CBC
laboratory results containing HGB, MCV, and MCH. Second, the CBC analysis results that detect blood
abnormalities are then matched with expert knowledge of thalassemia symptoms on PBS. Our system will
provide a prediction of the patient’s condition based on the analysis performed using CBC and PBS data.](https://image.slidesharecdn.com/3323576-250625093105-e471097b/85/Implementation-of-fuzzy-logic-approach-for-thalassemia-screening-in-children-2-320.jpg)
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Thus, the system can recommend the patient’s diagnosis result to the doctor. In this case, we use fuzzy
method to make thalassemia prediction using CBC and PBS data as an alternative to CBC and HPLC data.
We propose a new approach of using CBC and PBS to predict thalassemia as an alternative to CBC and
HPLC which are commonly used by doctors today. We evaluate it by comparing the performance of our
developed system with the opinion of an expert (clinical pathologist) and assess it is potential. We have
conducted a study using laboratory data with and without thalassemia to develop and evaluate a fuzzy
prediction method for thalassemia. Thalassemia screening using fuzzy approach with CBC and PBS data
input is our contribution in this study. The study results show that our model has a very good performance in
identifying thalassemia.
2. METHOD
2.1. Frameworks
The classification of diseases with a fuzzy approach is designed according to a predetermined
framework. The framework is a working conceptual description of the thalassemia classification system. The
framework that we have developed is presented in Figure 1. The main parts of Figure 1 are the knowledge
base, inference engine, and user interface. The knowledge base is built on the clinical pathologist’s
knowledge of blood disorders and thalassemia. The inference engine draws conclusions based on the
information stored in the knowledge base. The inference engine is responsible for reasoning about the
knowledge base and using it to solve problems or answer questions. The inference engine follows a set of
rules or logical steps programmed into the system. The user interface is in the form of a program display that
is designed according to its function. Doctors use the user interface to provide CBC and PBS input.
Furthermore, the doctor will get information on predictions of blood disorders and Thalassemia according to
the knowledge of a clinical pathologist.
Knowledge
base
Inference
engine
Thalassemia
prediction
Blood disorder
prediction
User interface
Doctor
CBC
PBS
clinical pathologist
Figure 1. Framework of thalassemia screening
2.2. Prediction of blood disorders
Doctors usually perform an anamnesis, a physical examination of the patient, and are supported by
the results of blood tests, i.e., CBC. This section uses a fuzzy approach to determine the suspected blood
disorder. The fuzzy approach is used because the opinion of doctors about the standard threshold values on
the CBC results has a tolerance. Based on previous literature [11], [16], CBC data: HGB, MCV, and MCH.
Based on our interviews, clinical pathologists believe that HGB is the first indicator used to suspect the
presence of blood disorders, then to assess other indicators.
2.3. Thalassemia prediction
The doctor’s diagnosis leads to a blood disorder or not blood disorder. If the diagnosis leads to
blood disorders, proceed with a more detailed blood test, namely PBS. Based on the results of the PBS, it will
then be compared with the characteristics of thalassemia disease. In general, the characteristics of thalassemia
based on PBS include microcytic, polychromasia, hypochromic, teardrop, and target cells [7], [29], [30]. The
thalassemia prediction section will provide output in the form of patient information indicated by thalassemia
or not thalassemia.](https://image.slidesharecdn.com/3323576-250625093105-e471097b/85/Implementation-of-fuzzy-logic-approach-for-thalassemia-screening-in-children-3-320.jpg)
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2.4. Knowledge based
This study uses CBC, including HGB, MCV, and MCH. The suspected presence of blood disorders
such as thalassemia can be known based on these values [31]. Table 2 is a knowledge base on the prediction
of blood disorders based on CBC.
Table 2. Knowledge-based for blood disorders based on CBC
No Parameters References Disorders units
1 HGB 10.8-12.8 <10.8 or >12.8 g/dL
2 MCV 73-101 <73 or >101 fL
3 MCH 23-31 <23 or >31 Pg
CBC values that do not match the reference values indicate that patients are strongly suspected of
having blood disorders. Doctors recommend that the patient undergo further examination, namely PBS, to
ensure that there are suspected blood disorders. Based on PBS [29], [30], [32], thalassemia has several unique
characteristics: microcytic, hypochromic, teardrop, polychromasia, and target cells. The opinion of expert
states that the patient will be called a carrier if he is microcytic and hypochromic. At the same time, it is
called thalassemia if it has other signs, such as teardrops, polychromasia, and target cells.
2.5. Fuzzy membership function
Based on the knowledge base on blood disorders, determine the parameters used and create a
mathematical function in a fuzzy membership function [33]. The existence of expert tolerance for the
reference value of each parameter used is why we use a fuzzy approach. We construct fuzzy membership
functions and curves for each CBC parameter [11] based on laboratory data on blood disorders presented in
Table 3.
Table 3. Membership function and curves
Parameter Membership function Curves
HGB
𝑥 [7.2] = {
1 𝑥 ≤ 10
10.8 − 𝑥
10.8 − 10
10 ≤ 𝑥 ≤ 10.8
0 𝑥 > 10.8
MCV
𝑥 [72] = {
1 𝑥 ≤ 71
73 − 𝑥
73 − 71
71 ≤ 𝑥 ≤ 73
0 𝑥 > 73
MCH
𝑥 [22.2] = {
1 𝑥 ≤ 21
23 − 𝑥
23 − 21
21 ≤ 𝑥 ≤ 23
0 𝑥 > 23
For example, we consider patient x with CBC data with HGB values of 7.20, MCV of 72.00, and
MCH of 22.20. Based on the membership function in Table 2, the fuzzy values for blood disorders are HGB:
1.00, MCV: 1.00, and MCH: 0.40. As for not blood disorders, the values are HGB: 0.00, MCV: 0.00, and
MCH:0.60. Then we create fuzzy weights for 40% HGB, 30% MCV, and 30% MCH. So, we get a fuzzy
weight for blood disorders of 0.82 and for not blood disorders of 0.18. Based on the fuzzy weights, it can be
concluded that there is a suspicion that the patient has a blood disorder.
Furthermore, a PBS-based examination was carried out on patients suspected of having blood
disorders. We put a “1” if the mark is found and a “0” if it is not found. Signs in question include microcytic,
hypochromic, teardrop, polychromasia, and target cells [30]. The PBS data of patient x showed microcytic: 1,
hypochromic: 1, teardrop: 1, polychromasia: 1, and target cells: 1. Based on the literature [29] and expert
knowledge, PBS is a method commonly used by doctors to diagnose blood disorders. Therefore, the
conclusion of the diagnosis based on the PBS value is that the patient suffers from thalassemia.](https://image.slidesharecdn.com/3323576-250625093105-e471097b/85/Implementation-of-fuzzy-logic-approach-for-thalassemia-screening-in-children-4-320.jpg)
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Figure 2. System evaluation metric for prediction of blood disorders
Table 6. Thalassemia prediction test results
No Parameter Expert Proposed system Results
Microcytic Hypochromic Teardrops Polychromasia Target cells
1 1 1 1 1 1 Thalassemia Thalassemia True
2 1 1 1 1 1 Thalassemia Thalassemia True
3 1 1 0 1 0 Thalassemia Thalassemia True
4 0 0 0 1 0 Not Not True
5 1 1 0 1 0 Thalassemia Thalassemia True
6 0 0 0 0 0 Not Not True
7 0 0 0 0 0 Not Not True
8 0 0 0 0 0 Not Not True
9 1 1 0 0 0 Thalassemia Thalassemia True
10 1 1 1 0 0 Thalassemia Thalassemia True
11 0 0 0 0 0 Not Not True
12 1 1 0 0 0 Thalassemia Thalassemia True
13 0 0 0 0 0 Not Not True
14 0 0 0 0 0 Not Not True
15 0 0 0 0 0 Not Not True
16 0 0 0 0 0 Not Not True
17 1 0 0 0 0 Not Not True
18 0 0 0 0 0 Not Not True
19 1 1 1 1 1 Thalassemia Thalassemia True
Evaluation must be done to measure the performance of the model we have developed. The
evaluation of the thalassemia classification that we propose gives results including 8 patients who are
thalassemia identified as thalassemia (true positive), 0 patients who are not thalassemia identified as
thalassemia (false positive), 0 patients who are thalassemia identified as not thalassemia (false negative), and
11 patients who were not thalassemia were identified as not thalassemia (true negative). We show the
confusion matrix in Table 7.
Table 7. Confusion matrix for prediction of thalassemia
Negative predictions Positive predictions
Actual negatives 11 0
Actual positives 0 8
Based on expert opinions as shown in Table 7, the accuracy of the test results of the system we
developed to identify Thalassemia using PBS is 100%. However, we feel that this research needs to be
developed further by using more datasets and developing models, for example with fuzzy ensembles [34],
[35]. We hope that this work can be continued by using more actual data to test the quality and accuracy of
the system that has been developed.
93%
95%
91%
95% 95%
88%
89%
90%
91%
92%
93%
94%
95%
96%
Accuracy Sensitivity or recall Specificity Precision F1-Score
RESULTS
TYPE OF EVALUATION](https://image.slidesharecdn.com/3323576-250625093105-e471097b/85/Implementation-of-fuzzy-logic-approach-for-thalassemia-screening-in-children-6-320.jpg)
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4. CONCLUSION
This study provides a novel fuzzy-based approach for thalassemia screening. It adopts a fuzzy-based
approach based on CBC and PBS data. The knowledge used in this research is also based on information
from clinical pathologists. The system has been evaluated using the hospital’s actual patient data and
comparing the results with those provided by experts. Based on CBC and PBS data, our system has very high
accuracy in predicting blood disorders (93% accuracy) and thalassemia (100% accuracy). However, more
data sets are needed to test this model. In the future, this model can adapt the fuzzy ensemble to improve
model performance.
ACKNOWLEDGEMENTS
We thank to Universitas Teknokrat Indonesia for supporting this research. We also thank the
Immanuel Hospital Bandar Lampung, Indonesia, especially Mr. Ns. Dwiantoro, for providing the laboratory
data.
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BIOGRAPHY OF AUTHORS
Erliyan Redy Susanto Doctoral student at the Faculty of Mathematics and
Natural Sciences, Lampung University. Currently he is a lecturer at the Faculty of Engineering
and Computer Science, Universitas Teknokrat Indonesia. He has his Bachelor of Computer
Science, Ahmad Dahlan University, Yogyakarta in 2006. He has his Master degree in
Computer Science from Bogor Agriculture University in 2016. His research interests are
primarily in the field of artificial intelligence for medicine. He can be contacted at email:
erliyan.redy@teknokrat.ac.id.
Admi Syarif Professor at the Dept. of Computer Science, Faculty of Mathematics
and Sciences, Lampung University, Indonesia. He received a Bachelor of Science in
Mathematics from Padjadjaran University, Indonesia, in 1990 and his Ph.D. in Industrial and
Information System Engineering from Ashikaga Institute of Technology, Japan, in 2004. He
was a director of the research center of Lampung University from 2010 to 2016; and has been
a national research reviewer of the Ministry of Education and Culture, Republic Indonesia,
since 2014. He can be contacted at email: admi.syarif@fmipa.unila.ac.id.
Warsito Professor at the Department of Physics, Faculty of Mathematics and
Natural Science, Lampung University. He completed his bachelor’s degree at the Department
of Physics, Brawijaya University, Malang in 1995. Meanwhile, his master’s degree was
completed in 2000 at Universite De Caen, France. For doctoral education he completed in
2004 at the same university in France. His other experience is as the Education and Culture
Attache at the Indonesian Embassy in Paris. Currently he serves as Deputy for Coordinating
Education Quality Improvement and Religious Moderation. He can be contacted at email:
warsito@fmipa.unila.ac.id](https://image.slidesharecdn.com/3323576-250625093105-e471097b/85/Implementation-of-fuzzy-logic-approach-for-thalassemia-screening-in-children-8-320.jpg)
Implementation of fuzzy logic approach for thalassemia screening in children
- 1. IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 13, No. 4, December 2024, pp. 4062~4070 ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i4.pp4062-4070 4062 Journal homepage: http://ijai.iaescore.com Implementation of fuzzy logic approach for thalassemia screening in children Erliyan Redy Susanto1,2 , Admi Syarif1 , Warsito1 , Khairun Nisa Berawi3 , Putu Ristyaning Ayu Sangging3 , Agus Wantoro2 1 Faculty of Mathematics and Natural Science, Lampung University, Bandar Lampung, Indonesia 2 Department of Information System, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia 3 Department of Biomolecular, Biochemistry and Physiology, Faculty of Medical, Lampung University, Bandar Lampung, Indonesia Article Info ABSTRACT Article history: Received May 22, 2023 Revised Nov 19, 2023 Accepted Dec 6, 2023 Thalassemia is one of the most dangerous blood disorders that can lead to severe complications. It is an inherited disease, usually detected after a child is two to four years old. Identification of thalassemia is a complex task, involving many variables. Doctors generally diagnose thalassemia by using a complete blood count (CBC) and high-performance liquid chromatography (HPLC) test results. However, HPLC tests are expensive and time- consuming, hence the need for other methods to identify thalassemia. There are many studies on the application of artificial intelligence for medical applications. In this study, we developed a new fuzzy-based approach to identify thalassemia based on a patient’s blood laboratory results. First, we analyzed the CBC data for blood disorder prediction. Secondly, we adopt the test results of peripheral blood smear (PBS) to identify whether the person has thalassemia. We conducted several experiments using 30 (thirty) hospital patient data and the results were compared with the results provided by experts. The experimental results show that the system can determine blood disorders with 93% accuracy and 100% precision in thalassemia prediction. This system is very effective to help doctors in diagnosing thalassemia patients. Keywords: Artificial intelligence Blood disorder Fuzzy approach Inherited disease Thalassemia This is an open access article under the CC BY-SA license. Corresponding Author: Admi Syarif Department of Computer Science, Faculty of Mathematics and Natural Sciences, Lampung University St. Prof. Dr. Sumantri Brojonegoro No.1, Bandar Lampung, Lampung 35145, Indonesia Email: admi.syarif@fmipa.unila.ac.id 1. INTRODUCTION Blood disorders are known to require very expensive medical expenses. Some blood disorders, such as thalassemia, hemophilia, blood clots, blood cancer, leukemia, lymphoma, and myeloma, require a lot of money. Diagnosing thalassemia is a challenging task. In addition to cancer, it is considered one of the most dangerous diseases in the world [1]. Thalassemia is one type of blood disorder due to genetic characteristics derived from both parents [2], [3]. Another possibility that it can cause is when a gene mutation occurs [4]. This disease is commonly known from infancy [5], [6]. In some cases, thalassemia is discovered after a person grows up [4]. When treatment is given late, the disease can cause growth delays in the child and other complications. Thalassemia is also divided into two categories: transfusion-dependent and nontransfusion- dependent [7]. The easiest way to perform a blood transfusion is by knowing the hemoglobin levels in the blood [7], [8]. If it is below the standard value, a blood transfusion should be initiated immediately. Thalassemia patients often experience anemia which can cause fatigue, weakness, and difficulty in
- 2. Int J Artif Intell ISSN: 2252-8938 Implementation of fuzzy logic approach for thalassemia screening in children (Erliyan Redy Susanto) 4063 performing physical activity. They may also experience delayed growth and abnormal bone development. Most of these cases are found in children over the age of two years. Thalassemia detection can be done through a blood test in a laboratory, commonly called screening. Thalassemia screening has several components of blood tests, including complete blood count (CBC) and high-performance liquid chromatography (HPLC) [9], [10]. Doctors usually use CBC to predict a blood disorder [11], [12]. Some of the CBC components that have been used include hemoglobin (HGB), mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) [13]. We found the problem that HPLC inspection is expensive, and the consumption time is prolonged [10], [14], [15]. Thus, our study aims to use a new fuzzy-based approach to minimize costs and accelerate the screening time for thalassemia. Artificial intelligence (AI) is one of the fields of computer science that is constantly developing and widely applied in various fields [16]. In multiple studies, AI has been widely used to solve problems related to medicine, agriculture, and business [17], [18]. It is also used to predict the presence of blood disorders [18], [19]. A significant problem in AI applications is dealing with non-deterministic data or information. One popular approach to this issue is to adopt a fuzzy approach. Since Zadeh [20] introduced it, fuzzy has been widely used for various applications to solve various problems of disease in humans [21], animals [22], and also plants [23]. A fuzzy approach to predicting thalassemia in children can overcome several challenges [24]. Fuzzy logic can help analyze interpretations that are incomplete, ambiguous, or subject to interpretation. This problem makes them suitable for dealing with thalassemia’s complex and diverse clinical picture. The proposed method uses a fuzzy approach to provide a more accurate and efficient method for predicting thalassemia in children. Thalassemia is very important to study and many studies on the use of AI for thalassemia problems have been conducted. The use of AI has addressed a number of challenges associated with thalassemia. We have summarized some AI approaches to solve some problems in thalassemia presented in Table 1. Table 1. AI research on thalassemia Year Methods Topics Authors 2010 Multi-layer perceptron, k-nearest neighbors, bayesian networks, naïve-Bayes, and multinomial logistic regression The effeciency of data types for classification performance of machine learning techniques for screening β-Thalassemia Paokanta et al. [25] 2016 Fuzzy logic Design of a fuzzy model for thalassemia disease diagnosis: using mamdani type fuzzy inference system (FIS) Thakur et al. [11] 2017 Fuzzy logic Thalassemia risk prediction model using fuzzy inference systems: an application of fuzzy logic Thakur and Raw [12] 2017 Fuzzy logic Using fuzzy logic for improving daily clinical care of β-Thalassemia patients Santini et al. [23] 2018 Support vector machine Detection of β thalassemia carriers by red cell parameters obtained from automatic counters using mathematical formulas Roth et al. [13] 2019 K-nearest neighbor and naïve Bayes Classification of thalassemia data using k-nearest neighbor and naïve Bayes Siswantining et al. [24] 2020 Random forest Classification of thalassemia data using random forest algorithm Aszhari et al. [17] 2020 Machine learning and deep learning Artificial intelligence in hematology: current challenges and opportunities Radakovich et al. [18] 2021 Ensemble classifiers Classification of β-thalassemia carriers from red blood cell indices using ensemble classifier Sadiq et al. [16] 2022 Machine learning and deep learning A review of artificial intelligence applications in hematology management: current practices and future prospects Alaoui et al. [19] Table 1 shows that various thalassemia problems worked a lot with AI. Some studies on AI with fuzzy logical approaches have excellent prediction accuracy [23], [26], [27]. Therefore, we use this approach to solve problems in thalassemia. This article aims to predict blood disorder in a child, focusing on thalassemia with a fuzzy approach. Our systems require data from laboratory tests such as CBC and peripheral blood smear (PBS). Thalassemia, referred to in this article, is a beta (β) type. This type of Thalassemia is most common in Indonesia and other Asian countries. This article perfected previous research on classifying Thalassemia [11], [28] by adding PBS as a new parameter. This article consists of two stages. First, the system detects blood disorders by analyzing CBC laboratory results containing HGB, MCV, and MCH. Second, the CBC analysis results that detect blood abnormalities are then matched with expert knowledge of thalassemia symptoms on PBS. Our system will provide a prediction of the patient’s condition based on the analysis performed using CBC and PBS data.
- 3. ISSN: 2252-8938 Int J Artif Intell, Vol. 13, No. 4, December 2024: 4062-4070 4064 Thus, the system can recommend the patient’s diagnosis result to the doctor. In this case, we use fuzzy method to make thalassemia prediction using CBC and PBS data as an alternative to CBC and HPLC data. We propose a new approach of using CBC and PBS to predict thalassemia as an alternative to CBC and HPLC which are commonly used by doctors today. We evaluate it by comparing the performance of our developed system with the opinion of an expert (clinical pathologist) and assess it is potential. We have conducted a study using laboratory data with and without thalassemia to develop and evaluate a fuzzy prediction method for thalassemia. Thalassemia screening using fuzzy approach with CBC and PBS data input is our contribution in this study. The study results show that our model has a very good performance in identifying thalassemia. 2. METHOD 2.1. Frameworks The classification of diseases with a fuzzy approach is designed according to a predetermined framework. The framework is a working conceptual description of the thalassemia classification system. The framework that we have developed is presented in Figure 1. The main parts of Figure 1 are the knowledge base, inference engine, and user interface. The knowledge base is built on the clinical pathologist’s knowledge of blood disorders and thalassemia. The inference engine draws conclusions based on the information stored in the knowledge base. The inference engine is responsible for reasoning about the knowledge base and using it to solve problems or answer questions. The inference engine follows a set of rules or logical steps programmed into the system. The user interface is in the form of a program display that is designed according to its function. Doctors use the user interface to provide CBC and PBS input. Furthermore, the doctor will get information on predictions of blood disorders and Thalassemia according to the knowledge of a clinical pathologist. Knowledge base Inference engine Thalassemia prediction Blood disorder prediction User interface Doctor CBC PBS clinical pathologist Figure 1. Framework of thalassemia screening 2.2. Prediction of blood disorders Doctors usually perform an anamnesis, a physical examination of the patient, and are supported by the results of blood tests, i.e., CBC. This section uses a fuzzy approach to determine the suspected blood disorder. The fuzzy approach is used because the opinion of doctors about the standard threshold values on the CBC results has a tolerance. Based on previous literature [11], [16], CBC data: HGB, MCV, and MCH. Based on our interviews, clinical pathologists believe that HGB is the first indicator used to suspect the presence of blood disorders, then to assess other indicators. 2.3. Thalassemia prediction The doctor’s diagnosis leads to a blood disorder or not blood disorder. If the diagnosis leads to blood disorders, proceed with a more detailed blood test, namely PBS. Based on the results of the PBS, it will then be compared with the characteristics of thalassemia disease. In general, the characteristics of thalassemia based on PBS include microcytic, polychromasia, hypochromic, teardrop, and target cells [7], [29], [30]. The thalassemia prediction section will provide output in the form of patient information indicated by thalassemia or not thalassemia.
- 4. Int J Artif Intell ISSN: 2252-8938 Implementation of fuzzy logic approach for thalassemia screening in children (Erliyan Redy Susanto) 4065 2.4. Knowledge based This study uses CBC, including HGB, MCV, and MCH. The suspected presence of blood disorders such as thalassemia can be known based on these values [31]. Table 2 is a knowledge base on the prediction of blood disorders based on CBC. Table 2. Knowledge-based for blood disorders based on CBC No Parameters References Disorders units 1 HGB 10.8-12.8 <10.8 or >12.8 g/dL 2 MCV 73-101 <73 or >101 fL 3 MCH 23-31 <23 or >31 Pg CBC values that do not match the reference values indicate that patients are strongly suspected of having blood disorders. Doctors recommend that the patient undergo further examination, namely PBS, to ensure that there are suspected blood disorders. Based on PBS [29], [30], [32], thalassemia has several unique characteristics: microcytic, hypochromic, teardrop, polychromasia, and target cells. The opinion of expert states that the patient will be called a carrier if he is microcytic and hypochromic. At the same time, it is called thalassemia if it has other signs, such as teardrops, polychromasia, and target cells. 2.5. Fuzzy membership function Based on the knowledge base on blood disorders, determine the parameters used and create a mathematical function in a fuzzy membership function [33]. The existence of expert tolerance for the reference value of each parameter used is why we use a fuzzy approach. We construct fuzzy membership functions and curves for each CBC parameter [11] based on laboratory data on blood disorders presented in Table 3. Table 3. Membership function and curves Parameter Membership function Curves HGB 𝑥 [7.2] = { 1 𝑥 ≤ 10 10.8 − 𝑥 10.8 − 10 10 ≤ 𝑥 ≤ 10.8 0 𝑥 > 10.8 MCV 𝑥 [72] = { 1 𝑥 ≤ 71 73 − 𝑥 73 − 71 71 ≤ 𝑥 ≤ 73 0 𝑥 > 73 MCH 𝑥 [22.2] = { 1 𝑥 ≤ 21 23 − 𝑥 23 − 21 21 ≤ 𝑥 ≤ 23 0 𝑥 > 23 For example, we consider patient x with CBC data with HGB values of 7.20, MCV of 72.00, and MCH of 22.20. Based on the membership function in Table 2, the fuzzy values for blood disorders are HGB: 1.00, MCV: 1.00, and MCH: 0.40. As for not blood disorders, the values are HGB: 0.00, MCV: 0.00, and MCH:0.60. Then we create fuzzy weights for 40% HGB, 30% MCV, and 30% MCH. So, we get a fuzzy weight for blood disorders of 0.82 and for not blood disorders of 0.18. Based on the fuzzy weights, it can be concluded that there is a suspicion that the patient has a blood disorder. Furthermore, a PBS-based examination was carried out on patients suspected of having blood disorders. We put a “1” if the mark is found and a “0” if it is not found. Signs in question include microcytic, hypochromic, teardrop, polychromasia, and target cells [30]. The PBS data of patient x showed microcytic: 1, hypochromic: 1, teardrop: 1, polychromasia: 1, and target cells: 1. Based on the literature [29] and expert knowledge, PBS is a method commonly used by doctors to diagnose blood disorders. Therefore, the conclusion of the diagnosis based on the PBS value is that the patient suffers from thalassemia.
- 5. ISSN: 2252-8938 Int J Artif Intell, Vol. 13, No. 4, December 2024: 4062-4070 4066 3. RESULTS AND DISCUSSION 3.1. Evaluation The systems we have developed have been tested to evaluate the results of this study. We have as many as 30 lab data sets of CBC and PBS data. The trial was conducted using patient data obtained from the hospital. The results are compared to the opinions of experts (clinical pathologist at a public hospital in Lampung Indonesia). The results of this experiment are presented in Table 4. Table 4. Test results predictive of blood disorders No Parameter Expert Proposed system Results HGB MCV MCH 1 12.90 73.70 25.90 Not Not True 2 5.90 49.30 12.00 Blood disorders Blood disorders True 3 7.20 72.00 22.20 Blood disorders Blood disorders True 4 9.20 98.30 30.80 Blood disorders Blood disorders True 5 13.00 82.30 29,10 Not Not True 6 11.70 59.90 18.80 Not Not True 7 11.80 74.30 27.60 Not Not True 8 8.80 90.50 28.90 Blood disorders Blood disorders True 9 4.50 85.70 29,20 Blood disorders Blood disorders True 10 13.70 79.20 27,60 Blood disorders Blood disorders True 11 11.20 83.50 28.00 Not Not True 12 12.60 78.80 26.80 Not Not True 13 11.30 76.40 25.50 Not Not True 14 15.50 83.60 29.90 Blood disorders Blood disorders True 15 13.80 82.20 28.60 Blood disorders Blood disorders True 16 12.50 77.50 27.60 Not Not True 17 7.30 53.50 14.90 Blood disorders Blood disorders True 18 8.70 72.90 23.30 Blood disorders Blood disorders True 19 9.70 82.10 26,40 Blood disorders Blood disorders True 20 16.70 65.90 21.70 Blood disorders Blood disorders True 21 3.60 91.70 27.30 Blood disorders Blood disorders True 22 11.30 69.00 10:60 Not Not True 23 10.00 92.20 30.10 Blood disorders Not False 24 13.90 84.00 28.90 Blood disorders Blood disorders True 25 4.90 110.10 38.00 Blood disorders Blood disorders True 26 6.70 77.20 25.00 Blood disorders Blood disorders True 27 3.50 80.20 27.80 Blood disorders Blood disorders True 28 5.00 66.70 20.30 Blood disorders Blood disorders True 29 10.80 62.50 19.90 Not Not True 30 14.00 74.00 25.60 Not Blood Disorders False Table 4 shows that clinical pathologists believe that 8 patients have blood disorders and 11 do not. The system we developed also predicts the same number. However, there are differences in predictions from data 23 and 30. The predictions from the two data sets are contradictory to each other. The classification system for blood disorders that we developed gave results including 18 patients identified as having blood disorders (true positive), 1 patient with not blood disorder identified as a blood disorder (false positive), 1 patient with blood disorder identified as not blood disorder (false negative), and 10 patients with not blood disorder were identified as not blood disorder (true negative). We present the confusion matrix in Table 5. Table 5. Confusion matrix for blood disorders Negative predictions Positive predictions Actual negatives 10 1 Actual positives 1 18 Table 5 shows that there were two inaccurate predictions, one wrong prediction for thalassemia and one wrong prediction for not thalassemia. We can evaluation metrics from these results, such as accuracy, sensitivity, and specificity. These show that the system has an accuracy value of 93.3%, a sensitivity or recall value of 94.7%, a specificity value of 90.9%, and a precision value of 94.7%. We illustrate the overall evaluation metric given by this system in the Figure 2. We conducted another experiment to evaluate the prediction of thalassemia. The prediction is based on PBS information about the patient. We have already compared the results with those given by clinical pathologists. This comparison is presented in Table 6.
- 6. Int J Artif Intell ISSN: 2252-8938 Implementation of fuzzy logic approach for thalassemia screening in children (Erliyan Redy Susanto) 4067 Figure 2. System evaluation metric for prediction of blood disorders Table 6. Thalassemia prediction test results No Parameter Expert Proposed system Results Microcytic Hypochromic Teardrops Polychromasia Target cells 1 1 1 1 1 1 Thalassemia Thalassemia True 2 1 1 1 1 1 Thalassemia Thalassemia True 3 1 1 0 1 0 Thalassemia Thalassemia True 4 0 0 0 1 0 Not Not True 5 1 1 0 1 0 Thalassemia Thalassemia True 6 0 0 0 0 0 Not Not True 7 0 0 0 0 0 Not Not True 8 0 0 0 0 0 Not Not True 9 1 1 0 0 0 Thalassemia Thalassemia True 10 1 1 1 0 0 Thalassemia Thalassemia True 11 0 0 0 0 0 Not Not True 12 1 1 0 0 0 Thalassemia Thalassemia True 13 0 0 0 0 0 Not Not True 14 0 0 0 0 0 Not Not True 15 0 0 0 0 0 Not Not True 16 0 0 0 0 0 Not Not True 17 1 0 0 0 0 Not Not True 18 0 0 0 0 0 Not Not True 19 1 1 1 1 1 Thalassemia Thalassemia True Evaluation must be done to measure the performance of the model we have developed. The evaluation of the thalassemia classification that we propose gives results including 8 patients who are thalassemia identified as thalassemia (true positive), 0 patients who are not thalassemia identified as thalassemia (false positive), 0 patients who are thalassemia identified as not thalassemia (false negative), and 11 patients who were not thalassemia were identified as not thalassemia (true negative). We show the confusion matrix in Table 7. Table 7. Confusion matrix for prediction of thalassemia Negative predictions Positive predictions Actual negatives 11 0 Actual positives 0 8 Based on expert opinions as shown in Table 7, the accuracy of the test results of the system we developed to identify Thalassemia using PBS is 100%. However, we feel that this research needs to be developed further by using more datasets and developing models, for example with fuzzy ensembles [34], [35]. We hope that this work can be continued by using more actual data to test the quality and accuracy of the system that has been developed. 93% 95% 91% 95% 95% 88% 89% 90% 91% 92% 93% 94% 95% 96% Accuracy Sensitivity or recall Specificity Precision F1-Score RESULTS TYPE OF EVALUATION
- 7. ISSN: 2252-8938 Int J Artif Intell, Vol. 13, No. 4, December 2024: 4062-4070 4068 4. CONCLUSION This study provides a novel fuzzy-based approach for thalassemia screening. It adopts a fuzzy-based approach based on CBC and PBS data. The knowledge used in this research is also based on information from clinical pathologists. The system has been evaluated using the hospital’s actual patient data and comparing the results with those provided by experts. Based on CBC and PBS data, our system has very high accuracy in predicting blood disorders (93% accuracy) and thalassemia (100% accuracy). However, more data sets are needed to test this model. In the future, this model can adapt the fuzzy ensemble to improve model performance. ACKNOWLEDGEMENTS We thank to Universitas Teknokrat Indonesia for supporting this research. We also thank the Immanuel Hospital Bandar Lampung, Indonesia, especially Mr. Ns. Dwiantoro, for providing the laboratory data. REFERENCES [1] J. I. Johnsen, C. 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- 9. ISSN: 2252-8938 Int J Artif Intell, Vol. 13, No. 4, December 2024: 4062-4070 4070 Khairun Nisa Berawi is a professor candidate from the Faculty of Medicine, University of Lampung. He earned his medical degree from Sriwijaya University, Palembang in 1999. In 2008, he completed his master’s degree in public health at Padjadjaran University, Bandung. She successfully completed her doctoral program in 2018 at Andalas University, Padang. She is a Sports Physiology Expert in Indonesia with an AIFO degree. She can be contacted at email: khairun.nisa@fk.unila.ac.id. Putu Ristyaning Ayu Sangging is a lecturer at the Faculty of Medicine, University of Lampung with expertise in Clinical Pathology. She completed medical education in 2002 at Hang Tuah University, Surabaya. Then she successfully completed her master’s degree in health and clinical pathology specialist in 2012 at Hasanuddin University, Makassar. Furthermore, he completed his sub-specialist education at Diponegoro University, Semarang. She can be contacted at email: ristya.ayu@gmail.com. Agus Wantoro is a Faculty of Engineering and Computer Science lecturer at the Universitas Teknokrat Indonesia. He obtained a bachelor’s degree in computer from the College of Informatics Management and Computer Teknokrat Lampung in 2009. He completed a master’s degree in computer in 2015 from Budi Luhur University, Jakarta. Then in 2022 complete his doctoral education at the University of Lampung. The field currently occupied is artificial intelligence in the medical field. He can be contacted at email: agus.wantoro@teknokrat.ac.id.