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American Journal of  Medical 
Science and Innovation (AJMSI) 

Risk Prediction of  Thalassemia Using Data Mining Classifiers
Khizra Ali1*, Muhammad Saqib1

Volume 2 Issue 2, Year 2023
ISSN: 2836-8509 (Online)

DOI: https://doi.org/10.54536/ajmsi.v2i2.1979
https://journals.e-palli.com/home/index.php/ajmsi

Article Information ABSTRACT

Received: August 17, 2023
Accepted: September 11, 2023
Published: September 22, 2023

Medical data mining is concerned with prediction knowledge, which is a useful method for 
extracting hidden patterns from given data for specific purposes. Thalassemia is one of  
the most common inherited blood hematological disorders, and this paper adopted data 
mining classification techniques to generate results with high performance and accuracy 
for risk prediction of  thalassemia. The dataset for this purpose was collected from NIBD 
(National Institute of  Blood Diseases), a well-known institute and hospital for blood 
diseases in Karachi, Pakistan. They provided 301 records of  CBC test reports containing 
positive and negative statuses of  diagnosis of  thalassemia traits. There were many instances 
in the report, of  which 6 were used for our research purpose, i.e. Gender, MCV, HGB, HCT, 
MCHC, and RDW. The dataset was divided into training and test data using the WEKA tool. 
Four algorithms of  data mining classification, namely J48 Decision Tree, Naïve Bayesian 
Network, SMO algorithm, and Multilayer Perceptron Neural Network were adopted to train 
the model and classify the patient having traits of  thalassemia from normal persons with 
the use of  the WEKA tool. Results revealed that out of  all four algorithms, Naïve Bayes 
provided results with the highest accuracy of  99%.

Keywords
Medical Data Mining, 
Thalassemia, J48 Decision 
Tree, Naïve Bayesian Network, 
Multilayer Perceptron Neural 
Network

1 Department of  Computer Science and Information Technology, NED University of  Engineering & Technology, University Road, 
  Karachi 75270, Pakistan
* Corresponding author’s e-mail: khizra.ali12@gmail.com

INTRODUCTION
One of  the most common genetic blood disorders, 
thalassemia, has received excessive attention in the 
research field of  medicine worldwide. Due to the absence 
or decreased production of  normal globin chains, a 
heterogeneous group of  hemoglobin disorders is called 
thalassemia syndromes. The disease is known to be the 
most common recessive worldwide as the population 
carriers of  genetic thalassemia mutation are 1-5% globally 
(Jameel et al., 2017; Origa, 2017). It is commonly found 
in Southeast Asia, the Middle East, North and Central 
Africa, and the Mediterranean (Herbert et al., 2009). In 
Pakistan, thalassemia carriers are more than 10 million, 
with a 5-7% prevalence rate, as about 5000 children 
yearly are diagnosed as thalassemia major carriers 
(Kamil et al., 2021; Khaliq, 2022).  Therefore, under 
this context, the greatest challenge for professionals in 
healthcare is to differentiate between normal individuals 
and asymptomatic thalassemia carriers (Alaa & Shurrab, 
2017; Jatoi et al., 2018). Thalassemia diagnosis depends 
on certain characteristics derived after performing a 
complete blood count (CBC) test. However, the reliability 
of  the test can lead to the misdiagnosis of  thalassemia as 
similar characteristics can also be observed in different 
blood disorders (Abdullah & Al-Asmari, 2016; Jatoi et al., 
2018; Meena et al., 2019).
Blood diseases can be of  various types, such as anaemia, 
which is a common nutritional deficiency and blood 
disorder in childhood and infancy, and iron deficiency 
anemia (IRD) is mostly found in women and children, 
especially in developing countries (AlAgha et al., 2018; Jatoi 
et al., 2018). However, the most crucial type of  anaemia is 
thalassemia, an inherited disorder whose identification or 
differentiation from normal patients is challenging from 

the CBC test (Abdullah & Al-Asmari, 2016). Therefore, 
the problem identified in the healthcare sector is to 
design a model that can predict the risk of  thalassemia 
in patients before their CBC test. This research explored 
data mining techniques using the CBC dataset, examining 
the similarities of  the different attributes categorizing 
normal traits or thalassemia traits for accurate detection 
and recognition of  optimal disease patterns. 
Multiple studies have been done worldwide regarding 
detecting different kinds of  blood disorders using data 
mining. However, researchers have used a different set 
of  instances of  the CBC test, and therefore, the data 
mining classifiers’ accuracy has varied (AlAgha et al., 
2018; Elshami & Alhalees, 2012). This also depends upon 
the choice of  data mining classifiers; Amin and Habib 
(2015) conducted a research to diagnose blood diseases 
using age and gender as dataset labels with MLP, J48, 
and Naïve Bayes algorithms (Amin & Habib, 2015). 
Similarly, Saichanma et al. (2014) used the J48 decision 
tree algorithm to predict the abnormality of  peripheral 
blood smear, focusing mainly on the attribute of  RBC of  
the CBC test (Saichanma et al., 2014). 
The previous studies (Abdullah & Al-Asmari, 2016; Alaa 
& Shurrab, 2017; AlAgha et al., 2018; Jatoi et al., 2018; 
Meena et al., 2019) have classified the types of  anaemia 
or thalassemia utilizing the techniques of  data mining. 
However, the present study focused on determining 
thalassemia traits’ existence based on the CBC test 
attributes (MCV, HGB, RDW, MCHC, and HCT) for 
predicting the risk of  thalassemia. The attributes used in 
this study were gender, MCV, HGB, HCT, MCHC, and 
RDW. The current research was aimed at risk prediction 
of  thalassemia using data mining classification algorithms. 
The selected classification algorithms for this study were 



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Naïve Bayes, Multilayer Perceptron, J48, and SMO using 
the WEKA data-mining tool. The research also focused 
on identifying the most potential classification algorithm 
for thalassemia by determining accuracy, precision, Recall, 
True Positive Rate, False Positive Rate, and F-measure 
using WEKA Experimenter.

LITERATURE REVIEW
Data Mining in Healthcare
Three basic data mining mechanisms include Decision 
Rules, Analysis, and Clustering (Classification). There 
are various applications of  data mining in healthcare 
that are effective for physicians to detect diseases by the 
extraction of  knowledge from data of  patients. It is a 
prominent field of  research and has extensive potential 
to revolutionise the medical environment and improve 
the quality of  clinical decisions (Asere & Botson, 2021; 
Saichanma et al., 2014). Tomar and Agarwal (2013) 
presented the importance of  data mining as the most 
interesting and motivating area of  research popular in 
health organisations. The role played by data mining is 
essential in uncovering the new trends in the healthcare 
domain and helpful for the associated field members. 
Results revealed that the best accuracy and performance 
of  the classification model are presented by feature 
selection (Tomar & Agarwal, 2013). 
Jothi and Husain (2015) illustrated that the most 
important step of  data mining is KDD for pattern 
discovery and extraction with the involvement of  a huge 
amount of  data. The paper reviewed various emerging 
models and techniques in this regard, and results from 
the paper review revealed that tasks of  classification, 
clustering, association rules, and anomaly detection are 
the most used methods in the medical sector. The widely 
used algorithms are decision tree, discriminant analysis, 
swarm intelligence, artificial neural network, k-nearest 
neighbour, association rule, and support vector rule (Jothi 
& Husain, 2015). Mdaghri et al. (2016) elaborated that data 
mining has been widespread in the many applications 
of  Clinical Decision Support Systems (CDDS) such as 
alerting systems, information retrieval, suggestion systems, 
reminders, and prediction models (Mdaghri et al., 2016).

Risk Prediction of  Thalassemia Using Data Mining
Jatoi et al. (2018) examined the core relationship between 
Thalassemia and Anemia from the CBC test. The 
relationship between the two was exploited to predict 
and identify the possibility of  getting thalassemia for 
patients already suffering from anaemia. The research 
was conducted in Pakistan, and the dataset of  CBC 
reports was collected from the Diagnostic and Research 
Laboratory of  LUMHS. Researchers with the application 
of  the Naïve Bayesian Network algorithm analysed and 
evaluated the dataset, and it was revealed in the final 
results that the Naïve Bayes algorithm has the best 
capability to detect or predict the core relationship 
between anaemia and thalassemia with an accuracy of  
98%. It was evaluated that out of  400 patients, 290 were 

infected with the disease, where 110 were normal. Results 
also reflected that if  patients have high or low values of  
MCV and MCH, then there are increased chances of  
thalassemia, and the conditions of  Iron deficiency and 
Vitamin B12 are slightly different in terms of  MCV and 
MCH (Jatoi et al., 2018).
Alaa and Shurrab (2017) applied data mining techniques 
to identify the relations between blood tumours and 
characteristics of  blood tests to predict disease at an 
early stage, as it can be used to enhance the ability to cure 
disease. Three data mining techniques were used, i.e. rule 
induction, association rules, and deep learning, to predict 
or classify normal patients from the ones suffering from 
blood tumours. Real-time data was collected from Gaza 
European Hospital in Palestine, and results showed that 
association rules provide the best relationship between 
blood tumour and blood test characteristics. However, 
deep learning classifiers are best for predicting tumour 
types, providing an accuracy of  79.45%. Association rules 
best describe both normal haematology and tumours in 
blood and are identified as follows in the current research 
using the inductive rules method (Alaa & Shurrab, 2017).
Abdullah and Al-Asmari (2016) specified the types of  
anaemia for anaemic patients by using classification 
algorithms of  data mining and constructing a predictive 
model. The dataset was made by filtering and eliminating 
the variables that were not desired, and then some 
classification algorithms such as SMO, Multilayer 
Perceptron, Naïve Bayes, and J48 Decision Tree were 
applied to the WEKA tool of  data mining to present 
results. After performing several experiments, the 
WEKA experimenter has proven that the best accuracy is 
provided by the J48 Decision Tree algorithm, giving the 
best potential classification of  anaemia types. Evaluation 
of  data from 41 patients was done with several 
percentage splits of  the dataset, i.e. 20%, 40%, and 60%. 
Furthermore, comparing the results for four algorithms, 
it was observed that the best accuracy of  93.75% was 
obtained from SMO and J48 Decision Tree (Abdullah & 
Al-Asmari, 2016).
AlAgha et al. (2018) collected data on patients suffering 
from thalassemia from the Palestine Avenir Foundation 
to work on presenting a model for overcoming the 
challenges of  thalassemia. The study aimed to predict a 
special type of  thalassemia (β-thalassemia) based on the 
hybrid data mining model. Additionally, to overcome the 
problem of  imbalanced class distribution in the dataset, a 
SMOTE technique was first used and applied to deal with 
this problem. The second step utilised four classification 
models to differentiate between patients carrying 
carriers of  β-thalassemia and normal persons with the 
help of  different evaluation metrics. The classification 
models used were Decision Tree, Naïve Bayes, k-nearest 
Neighbour (k-NN) and Multilayer Perceptron neural 
network. Naïve Bayes provided the results with the 
highest accuracy differentiating between a normal person 
and thalassemia patients with the sampling SMOTE ratio 
of  more than 400%, revealing a sensitivity of  98.81% and 



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specificity of  99.47% (AlAgha et al., 2018). 
Meena et al. (2019) developed a decision support system 
using data mining techniques as data was collected from a 
survey conducted by the Indian Government in 2015-16 
called NFHS-4. The collected data was utilised to predict 
anaemia among children and create a relationship between 
a mother’s diet during pregnancy and health affecting the 
child’s anaemic status. In this way, clinicians and parents 
can understand the influence of  the practices of  feeding 
by the mother are fine, and they are following guidelines 
to prevent anaemia. The two techniques of  association 
rule and decision tree were applied and compared for the 
mentioned aim, and a model of  knowledge discovery was 
proposed using artificial intelligence to propose systems 
based on medical experts’ advice. The architecture of  
the proposed system depicted the stages of  the data pre-
processing tier, processing tier using decision tree and 
association rules, and user tier (Meena et al., 2019). 
Elshami and Alhalees (2012) performed different 
experiments to identify the most significant classifier 
useful for differentiating between the multiple types of  
thalassemia (Normal, Thal-I, Thal-M, Iron Def/Thal-T, 
Other). Results revealed that neural networks provided 
the best classification outputs for detecting or diagnosing 
different types of  thalassemia compared to Naïve Bayes 
and Decision Tree. Almost all the experiments supported 
the main feature of  MCV to indicate thalassemia 
existence, where the indicator value was identified to be 
less than 77.65. Furthermore, if  the age is greater than 12 
and the value of  MCV is greater than 77.65, thalassemia is 
not possible in an individual. The results were bright, with 
an accuracy greater than 90% (Elshami & Alhalees, 2012).
Hasani and Hanani (2017) investigated the three types of  
anaemia, including α-thalassemia trait, β-thalassemia trait 
and iron deficiency anaemia, as detecting them is crucial 
due to their similar blood characteristics. Also, the tests for 
their diagnosis are costly and time-consuming; therefore, 
an accurate model for classifying and diagnosing kinds of  
anaemia is important. The study used five classification 
algorithms on WEKA software to suggest an algorithm 
that provides the lowest mean absolute error and 
highest accuracy. Results of  the research indicated that 
combining Naïve Bayes, IBK and J48 algorithms using 
a voting algorithm with all the features provided the 
highest accuracy of  96.343% and 96.2169% (Hasani & 
Hanani, 2017).
Egejuru et al. (2017) predicted the risk of  thalassemia in 
different age groups, and knowledge regarding the risk 
factors was collected by carrying out a structural interview 
with experienced medical personnel, and a questionnaire 
was used to collect data regarding the related parameters. 
The environment used for applying supervised machine 
learning algorithms was WEKA, and the predictive 
model was validated for diagnosing thalassemia. Data 
was collected from 51 patients, and dataset parameters 
included demographics and clinical variables. Age, 
Gender, Marital Status, Social Class, and ethnicity were 
demographic variables, whereas spleen enlargement, 

urine colour changes, family history, diabetes and parent 
carriers were clinical variables. Results showed that the 
distribution of  risk of  thalassemia was 31% high cases, 
16% moderate cases, 10% low cases and 43% no cases. 
It was concluded in this study that for the prediction 
of  thalassemia, multilayer perceptron will improve the 
process of  decision-making within the healthcare sector 
regarding thalassemia diagnosis (Egejuru et al., 2019). 

MATERIALS & METHODS
Dataset and Pre-processing
Data mining also termed knowledge discovery in 
databases, is a useful tool for extracting knowledge 
containing the stages of  data pre-processing, pattern 
recognition and classification (Singhal & Jena, 2013). 
Data pre-processing is about filtering the data by 
removing the no-interest attributes and reducing noise or 
inconsistencies such as missing values or outliers (Sultana 
et al., 2016). The stages of  pre-processing of  data utilised 
in this research are shown in Figure 1 below.

Figure 1: Flowchart of  Data Pre-processing Stages

The pre-processing of  data provides data quality with 
high accuracy, completeness, consistency, interpretability 
and timeliness (Singhal & Jena, 2013). As shown in Figure 
1, data cleaning is the stage to check inconsistency and 
eliminate incorrect values, and data integration includes 
combining data from all databases, data reduction decreases 
the volume of  unnecessary data and data transformation is 
converting into the format supported by the software used 
(Sharma et al., 2012; Singhal & Jena, 2013). 
In the context of  this research, diagnosis of  thalassemia 
was conducted by collecting data from CBC test reports 
from the National Institute of  Blood Diseases (NIBD) 
Karachi. CBC (Complete Blood Count) test is commonly 
ordered as a simple blood test for a routine medical 
assessment (Ogasawara et al., 2019). The attributes shown 
in Table 1 contain all the essential parameters of  CBC 
tests, Blood components and their reference range. The 
number of  RBCs, the total amount of  haemoglobin in 
the blood, the average size of  an RBC, i.e. MCV, and 
the amount of  space taken by an RBC in the blood, i.e. 
Haematocrit (Mekić et al., 2018; Yin et al., 2020). The 
other measurements, i.e. the concentration (MCHC) and 
the amount (MCH) of  haemoglobin, also contain useful 



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results of  normal persons and patients suffering from 
thalassemia. The dataset included 1 nominal, whereas 
5 numerical values and one column presented patients’ 
status, i.e. POSITIVE or NEGATIVE. Later, the dataset 
was divided into training and test data by applying class 
“Resample, “ a supervised instance filter on WEKA 
software used to produce a random subsample of  a 
dataset with or without replacement.

Data Analysis
WEKA Software
The algorithms used to predict and diagnose thalassemia 
were applied to the pre-processed dataset on WEKA 
software. WEKA is a popular machine learning software 
developed at the University of  Waikato, New Zealand, 
containing a collection of  algorithms and visualization 

Table 1: CBC Test Parameters (Source: Dataset)
Blood component Abbreviation Reference range
Haemoglobin* HGB Male: 13.5-17.5 g/dL

Female: 12.0-16.0 g/dL
Hematocrit* HT Male: 41%-53%

Female: 36%-46%
Mean corpuscular volume MCV 80-100 μm3
White blood cells WBC 4500-11,000/mm3
Red blood cells* RBC Male: 4.3-5.9 million/mm3

Female: 3.5-5.5 million/mm3
Mean corpuscular haemoglobin MCH 25.4-34.6 pg/cell
Mean corpuscular haemoglobin concentration MCHC 31%-36% Hb/cell
Red Cell Distribution Width RDW Male: 11.6-14.6%

Female: 12.2 to 16.1%
Platelets Platelets 150,000-400,000/mm3

information about RBCs. These features of  the CBC test 
can also be explained as Red Blood Cells (RBCs) carry 
oxygen, Hemoglobin (HB) is in RBC as an oxygen-carrier 
protein, White Blood Cells (WBC) are required to fight 
infections in the human body, and Hematocrit (HCT) 
represents the red blood cells percentage into the plasma 
or fluid component in Platelets of  blood that causes 
blood clotting (Ogasawara et al., 2019; Yin et al., 2020). 
The test diagnoses and monitors the different types of  
blood diseases (e.g. anemia, thalassemia, iron deficiency), 
inflammatory diseases, infection, and malignancy (Mekić 
et al., 2018; Ogasawara et al., 2019).
There are many attributes in the CBC test reports, but 
only 6 attributes were selected for the risk prediction 
of  thalassemia via WEKA software shown in Table 
2. These six attributes include Gender, MCV, HCT, 
HGB, MCHC, and RDW and the ranges considered to 
differentiate between normal persons and thalassemia 
patients are shown in Table 1. Furthermore, the dataset 
was transformed into CSV and ARFF file format 
as supported by WEKA software. The data of  301 
patients was provided by NIBD, containing the CBC test 

Table 2: Dataset Attributes Used for Classification
Attribute Attribute Value Attribute Category
Gender Male 1

Female 2
MCV <80 Microcytic

80-100 Normal
HGB <10 Severe

10-12 Normal
HCT <37 Low

37-50 Normal
MCHC <32 Hypochromic

32-36 Normochromic
RDW >14.6 High

11.6-14.6 Normal Figure 2: The Proposed Method



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tools (Jovic et al., 2014; Sultana et al., 2016). These are 
used for predictive modelling and data analysis together 
with graphical user interfaces to get easy access to its 
functionalities. WEKA tool is platform-independent, 
easily available, and open source (Singhal & Jena, 2013). 
In this research, WEKA explorer and experimenter 
were used to explore data and perform experiments and 
statistical tests. The implementation of  the proposed 
method began by using four classifiers on WEKA on 
training and testing datasets to validate results with the 
highest accuracy and least mean square error (Jovic et al., 
2014). After classification on WEKA explorer, generated 
results were evaluated in the WEKA experimenter. Figure 
2 below is the research flowchart which explains the 
proposed method.
Figure 2 above presents the steps followed for the 
execution of  the proposed method. The process begins 
with collecting data, identifying the attributes, uploading 
the dataset on WEKA in CSV or ARFF format, applying 
various classification algorithms, predicting results, and 
evaluating the performance of  each classifier is identified 
from the accuracy, precision, Recall, true positive rate, etc.

The Algorithms Used for Classification
J48 Decision Tree
A decision tree is a supervised classification algorithm 
that verifies a problem and determines the dependent 
variable values based on independent values via powerful 

approaches to data mining and knowledge discovery 
(Drazin & Montag, 2012; Sahu & Mehtre, 2015). J48 
algorithm uses a divide and conquer approach to grow a 
tree, checking the cases belonging to the same class and 
labelling a leaf  with that class (Drazin & Montag, 2012). 
Furthermore, Figure 3 presents the flowchart of  the J48 
decision tree algorithm, presenting the main purpose of  
the algorithm as a data mining classifier. It is a predictive 
machine learning model that decides conditions based 
on the training dataset’s attributes. Further, it provides a 
pruned decision tree useful for addressing overfitting and 
classifying instances correctly. It splits the values based 
on the threshold specifying what is upper than, equal 
to, or less than that value, especially when dealing with 
continuous attributes (Sahu & Mehtre, 2015). 

Naïve Bayes Classifier
Naïve Bayes scans the training data and estimates all 
the probabilities with high learning efficiency (An et al., 
2017). The classification in this algorithm is based on the 
assumptions for identifying the object that is likely to be 
classified in the identified category, showing a direct or 
inverse relationship between one conditional probability 
(An et al., 2017; Xu, 2018).

Figure 3: Flowchart for J48 Decision Tree Algorithm

Figure 4: Flowchart for Naïve Bayesian Algorithm

Figure 4 illustrates the generative process of  the Bayesian 
classifier, where each arrow represents conditional 
dependency among variables (Xu, 2018). It computes 
a probability after calculating the rate of  values and 
their combinations in a specific dataset. Given the 



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probability of  a single event, the Naïve Bayes classifier 
identifies another event that has already happened 
(Granik & Mesyura, 2017). The algorithm uses kernel 
density estimators dealing with numeric attributes using 
supervised discretization (An et al., 2017; Jabbar & 
Samreen, 2016). 

SMO Algorithm
SMO is an outstanding SVM algorithm in memory 
and efficiency requirements abbreviated as Sequential 
Minimal Optimization. It requires cross-validation 
to avoid overfitting and optimize parameters in the 
mathematical model (Luo et al., 2016). SMO in WEKA 
is a supervised Support Vector Machine algorithm that 
analyses data and recognizes patterns. Furthermore, the 
SVM algorithm also possesses the same functional form 
of  neural networks and functions as a radial basis (Zhang 
et al., 2018). The working of  the SMO algorithm is shown 
in Figure 5.

belonging to two classes is given with associated class 
labels, and the algorithm of  SMO was applied using the 
WEKA tool to train the model.

Multilayer Perceptron
One of  the significant models in artificial neural network 
(ANN) is the multilayer perceptron, which contains a 
single input layer, one or more than one hidden layer 
and a single output layer; the neurons are organised in 
these layers which are not connected in the same layer 
(Singh et al., 2018). The values from the input node pass 
to the first hidden layer, and the same process continues 
with all the hidden layers until outputs are produced. 
Furthermore, the neurons in the input layer are the same 
as measurement variables for pattern problems, whereas 
the number of  classes equals the number of  neurons in 
the output layer (Ramchoun et al., 2016; Singh et al., 2018). 
The corresponding input generates the desired output in 
ANN, and the results can be viewed by computing the 
difference between the training set’s desired output and 
the network’s output on test data (Kwon et al., 2017; Singh 
et al., 2018). The multilayer perceptron is the most utilised 
form of  neural network used as a back-propagation 
training algorithm. Excess connections can create a 
problem of  overfitting; however, a lack of  connections in 
the neural network may face the problem of  insufficient 
parameters (Ramchoun et al., 2016). In this research, the 
multilayer perceptron model was used, a feed-forward 
neural network model that maps the input data into 
desired or suitable outputs. The working of  the multilayer 
perceptron algorithm is shown in Figure 6.

Figure 5: Flowchart for SMO Algorithm

SMO is generally made for a two-class classification 
problem to analyse the greatest separation between two 
classes, discovering the maximum distance to the nearby 
point. It has also been studied that SVM algorithms are 
based on the advances of  the theory of  machine learning 
in a high dimensional feature space that uses a hypothesis 
space of  linear function and implements a learning bias 
derived from statistical learning theory (Luo et al., 2016; 
Zengin et al., 2017). In this research, a set of  training data 

Figure 5: Flowchart for SMO Algorithm

Results & Discussion
J48 Decision Tree
The training data set was uploaded on WEKA Explorer, 
and after applying the J48 decision tree classification 
algorithm, the results derived are shown in Figure 7. 



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Figure 7: J48 Decision Tree

The decision tree in Figure 7 above reflects the rules 
that predicted the positive or negative risk of  having 
thalassemia traits in the patient. The tree size is 7, whereas 
the number of  leaves is 4. The WEKA tool constructed 
the pruned tree under the conditions stated in Figure 8.
Based on the J48 decision tree results per the trained 
model, rules and decisions are listed below in Table 3. 
The results were derived by showing the status of  CBC 
test results as POSITIVE (Persons having traits of  
thalassemia) or NEGATIVE (Normal).

There are different parameters based on which the 
accuracy of  the results is defined stated below:
Relative Absolute Error: It is the way that provides the 
measure of  the performance of  a trained or predictive 
model used in data mining and machine learning. It is a 
general measure of  accuracy or precision expressed as 
a ratio as a result of  comparing a mean error to errors 
produced by the naïve model (Iyer et al., 2015).

Confusion Matrix
The performance of  a classification model is described 
by the confusion matrix on test data by trained data for 
which true values are known (Singh et al., 2018). Besides, 
to measure the effectiveness of  the classification model 
confusion matrix presents different combinations of  
actual or predicted values. The four entries of  the 
confusion matrix are defined below:
TP Rate: The number of  entries/ records classified as 
true and true in actuality represents the true positive rate 
(Singh et al., 2018).
FP Rate: The number of  entries/ records classified as 
true and false in actual represents the false positive rate 
(Singh et al., 2018).
FN Rate: The number of  entries/ records classified as 
false and where they were true in actual represents the 
false-negative rate (Singh et al., 2018).
TN Rate: The number of  entries/records classified as 
false and were false in actual represents the true negative 
rate (Singh et al., 2018).
Accuracy: It is the ratio of  the number of  correct 
predictions to the total number of  predictions (Wang & 
Li, 2019).

Figure 8: Decision Tree Rules

Table 3: Thalassemia Classification Rules
Rules Decision
IF (MCV > 78.3) then, NEGATIVE
Else if  (MCV <= 78.3 and AND RDW 
> 14.3) then,

POSITIVE

Else if  (MCV <= 78.3 and AND RDW 
<= 14.3 AND MCHC > 31.7) then,

NEGATIVE

Else if  (MCV <= 78.3 and AND RDW 
<= 14.3 AND MCHC <= 31.7) then,

POSITIVE
Precision: It analyses how many are actually positive out 
of  all the correctly predicted positive classes (Alam et al., 
2022; Wang & Li, 2019).



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Recall: It is also known as the probability of  detection, 
sensitivity, or TP rate and detect how much were correctly 
predicted out of  all positive classes (Alam et al., 2022; 
Wang & Li, 2019).

F-measure: The weighted average of  Recall and precision is 
known as F-measure (Alam et al., 2022; Wang & Li, 2019).

validation. The rate of  correctly classified instances was 
97%, whereas the rate for incorrectly classified instances 
was 6%. The relative absolute error of  the model was 
computed as 7.4438%. The detailed accuracy of  the model 
by class is depicted in the form of  TP Rate, FP Rate, 
Precision, Recall and F-measure. The confusion matrix 
in Figure 10 represents that the instances classified as 
POSITIVE are 117 (TP), whereas 77 (TN) were classified 
as NEGATIVE. The TP Rate, Precision, Recall and 
F-measure were derived as 0.975 for Class POSITIVE and 
0.963 for Class NEGATIVE.
Furthermore, the test data was supplied on the J48-
trained model to predict/diagnose thalassemia traits and 
visualize results. The results were transformed into a 
CSV file, and Table 4 shows the sample of  17 records 
of  test data out of  101 evaluated using the trained model 
showing the ‘predicted margin’ and ‘predicting status’ for 
risk prediction of  thalassemia.

Figure 9 presents the results of  the J48 model developed 
using WEKA software. The resampling procedure for 
evaluating data mining tools is cross-validation, referring to 
the number of  groups for the given dataset to be splitted. 
For the evaluation of  different data mining algorithms in 
this research, the dataset was divided into 10-fold cross-

Figure 9: J48 Results

Table 4: J48 Classifier Output Results using WEKA Tool
MCV HCT HGB MCHC RDW Gender ‘prediction margin’ ‘predicted Status’
66.4 4.9 22.3 29.5 31.5 2 1 POSITIVE
74.6 4.9 19.3 22.6 33.7 1 1 POSITIVE
69 9.3 21.7 26.7 32.6 1 1 POSITIVE
73.1 7.4 25.8 32 26.4 2 1 POSITIVE
77.3 6.8 34.2 31.3 33.6 1 1 POSITIVE
71.2 6.8 21.5 30.1 36.7 1 1 POSITIVE
80.1 10.5 11.6 35.2 42.1 1 -0.974359 NEGATIVE
75.3 6.7 17.6 27.5 31.5 2 1 POSITIVE
76.4 6.9 27.7 25.9 34.1 1 1 POSITIVE
69 9.4 21.9 27.4 29.6 1 1 POSITIVE
68.9 9.9 19.9 31.2 26.9 1 1 POSITIVE
73.1 9.1 21.4 30.5 32.1 2 1 POSITIVE
83.5 10.1 11.7 33.6 42.4 1 -0.974359 NEGATIVE



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82.4 10.3 12.7 33.6 44 1 -0.974359 NEGATIVE
57.9 5.9 16.9 30.1 33.5 1 1 POSITIVE
82.6 11.9 14 33.9 43.2 1 -0.974359 NEGATIVE
80.9 12.4 13.6 33.5 41.2 1 -0.974359 NEGATIVE

Naïve Bayes Classifier
The results for the Naïve Bayes classifier are shown in the 
Figure 10 below.
After applying the Naïve Bayes algorithm on the WEKA 
tool, the correctly classified instances based on the given 
data were 99%; however, only 1% of  data was incorrectly 
classified. Furthermore, as shown in Figure 5.6, the relative 
absolute error of  Naïve Bayes results was 1.445%. After 

applying the Naïve Bayesian algorithm on the training 
dataset, results showed that there is only one entry for FP 
and FN, suggesting that the model predicting thalassemia 
with a 99.0 % accuracy rate; therefore, the TP rate for 
Class POSITIVE was identified as 99.2% and 98.8% for 
Class NEGATIVE suggesting that 119 CBC test results 
for thalassemia are positive where 79 are negative based 
on the given data.

Figure 10: Naïve Bayes Results

SMO Algorithm
The results for the SMO Algorithm are represented in 
Figure 11, analysed using the WEKA tool.
The results derived from the SMO model on the given 

dataset provided results with 3.1236% relative absolute 
error, where 98.5% were correctly classified instances, 
whereas 1.5% were incorrectly classified instances. 
Moreover, as shown in figure 5.7, after applying the 

Figure 11: SMO Algorithm Results



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SMO algorithm on the training dataset, results showed 
that there are three entries for FP and no entry for FN, 
suggesting that the model predicting thalassemia with 
a 98.5 % accuracy rate; therefore the TP rate for Class 
POSITIVE was identified as 97.5% and 100% for Class 
NEGATIVE suggesting that 117 CBC test results for 
thalassemia are positive where 80 are negative based on 
the given data.

Multilayer Perceptron
After applying a Multilayer Perceptron Neural Network 
on the training dataset, the results shown in the Figure 12 
below were derived.

Figure 5.8 presents the results for training data using the 
Multilayer Perceptron classifier on the WEKA tool. The 
algorithm’s rate of  correctly classified instances is 98%, 
whereas the rate for incorrectly classified instances is 2%. 
As shown in Figure 5.8, the relative absolute error for the 
trained model was derived to be 3.5101%. Furthermore, 
it shows that there are three entries for FP and only one 
entry for FN, suggesting that the MLP model predicts 
thalassemia with a 98.0 % accuracy rate; therefore, the 
TP rate for Class POSITIVE was identified as 97.5% 
whereas 98.8% for Class NEGATIVE suggesting that 
117 CBC test results for thalassemia are positive and 79 
are negative based on the given data.

Figure 12: MLP Results

Comparison of  Algorithms’ Results through WEKA 
Experimenter
This section of  the report compares all four classifiers 
using the WEKA experimenter based on precision, Recall 
and F-measure. A paired t-test was performed to identify 
the difference between the algorithms used. It was used in 
this research using the WEKA experimenter to compare 

the results among all four algorithms. The data mining 
measures that are useful to analyse the performance of  
each algorithm are precision, Recall and F-measure.
Figures 13, 14 and 15 illustrate the WEKA experimenter’s 
snapshots using precision, Recall, and F-measure. Paired 
t-test was applied in these experiments to identify the 
algorithm with the highest accuracy for the given dataset. 

Figure 13: Comparison of  J48, NB, SMO, and MLP algorithms using WEKA experimenter using Precision



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Figure 14: Comparison of  J48, NB, SMO, and MLP algorithms using WEKA experimenter using Recall

Figure 15: Comparison of  J48, NB, SMO, and MLP algorithms using WEKA experimenter using F-measure

It was evaluated that Naïve Bayes performed the risk 
prediction of  thalassemia with the highest performance 
among all four classifiers with precision, Recall and 
F-measure of  99.0%. The comparative performance 
for all four classifiers is also visualised based on relative 
absolute error, as shown in the figure below.

It can be viewed in Figure 16 that the least root mean 
square error of  1.445% was for the Naïve Bayes model in 
comparison with models of  J48, SMO and MLP. Hence, 
it proved that based on the given dataset, Naïve Bayes 
provided results with the highest accuracy and least mean 
square error for the risk prediction of  thalassemia.

Figure 16: Comparison of  J48, NB, SMO, and MLP algorithms using WEKA experimenter using F-measure



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CONCLUSION
A rapid increase is observed in inherited haemoglobin 
disorders, and despite the efforts to control the spread of  
these diseases, the number of  major cases leading to death 
is increasing. Thalassemia is one of  these inherited blood 
disorders that has received excessive attention over the 
years in medical data mining. Diagnosis of  thalassemia is 
based on some characteristics derived after performing the 
CBC test. Therefore, various data mining techniques were 
utilised in this research to identify the hidden patterns in 
the given dataset of  CBC test results for thalassemia. The 
research was conducted for the diagnosis or risk prediction 
of  thalassemia using data mining classification algorithms. 
The selected classification algorithms for this study were 
Naïve Bayes, Multilayer Perceptron, J48 and SMO using 
the WEKA data-mining tool.
Further, it also analysed the best potential classification 
algorithm for thalassemia by determining accuracy, 
precision, Recall, and F-measure using WEKA 
Experimenter. The data of  301 CBC results provided by 
NIBD was divided into training and test data, and it was 
pre-processed by applying a resample filter on the WEKA 
tool. For the evaluation of  different data mining algorithms 
in this research, the dataset was categorised into 10-fold 
cross-validation. The results were transformed into a CSV 
file showing the ‘predicted margin’ and ‘predicting status’ 
for risk prediction of  thalassemia. Results revealed that 
after applying four different algorithms on the training 
dataset, the Naïve Bayes model predicted thalassemia with 
the highest accuracy of  99.0 % accuracy rate suggesting 
that 119 CBC test results for thalassemia are positive, 
where 79 are negative based on the given data.

Future Work
This research was conducted by applying different 
classification algorithms to get the best prediction or 
diagnosis of  thalassemia based on the dataset of  301 
constructed of  CBC results. In the future, the research 
work can be expanded by using more types of  data mining 
algorithms, such as clustering or association rules, to 
identify the best-performing algorithm based on the given 
dataset. Association rules can be used to identify important 
relations among different attributes of  the given dataset 
to detect the type of  blood disease. Furthermore, in the 
future, the aim of  the analysis could be slightly different, 
such as identifying the survival rate of  thalassemia 
patients, classifying between anaemia and thalassemia, or 
developing a classification model for the different types 
of  thalassemia. Moreover, a big dataset can be selected 
or requested from NIBD or any other organization in 
Pakistan to present more genuine and authentic results, 
training the model with the highest possible accuracy. 
In addition, the big dataset can be divided into training 
and test data by setting up different split-up percentages 
for training data to analyse which dataset is capable 
of  the highest performance results. Future work can 
be conducted by comparing the CBC report’s other 
parameters and identifying the core relationship with the 

variables that can cause different blood diseases. The data 
mining techniques are very powerful, but they should be 
used with great care in the field of  medicine, and therefore, 
there is a need to discover the best mining algorithm for 
the specified medical area in the future.

Acknowledgements 
We express our deepest gratitude to our independent 
research project supervisor, Dr. Raheela Asif  (Department 
of  Software Engineering, NED University of  Engineering 
& Technology, University Road, Karachi 75270, Pakistan), 
for her relentless assistance throughout the research. We 
are also grateful to NIBD (National Institute of  Blood 
Diseases), Karachi City, Sindh, Pakistan, for providing 
data for this research.

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