Comparative Analysis of Classification Algorithms using Weka

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 6 Issue 5, July-August 2022 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD50568 | Volume – 6 | Issue – 5 | July-August 2022 Page 858
Comparative Analysis of Classification Algorithms using WEKA
Sakshi Goel1
, Neeraj Kumar2
, Saharsh Gera3
1
M Tech Scholar, CSE, MERI College of Engineering & Technology, Sampla, Haryana, India
2,3
Assistant Professor, CSE, MERI College of Engineering and Technology, Sampla, Haryana, India
ABSTRACT
Data Mining is the process of drawing out the useful information
from the raw data that is present in various forms. Data Mining is
defined as study of the Knowledge Discovery in database process or
KDD. Data mining techniques are relevant for drawing out the useful
information from the huge amount of raw data that is present in
various forms. In this research work different types of classification
algorithms accuracies are calculated which are widely used to draw
the significant amount of data from the huge amount of raw data.
Comparative analysis of different Classification Algorithms have
been done using various criteria’s like accuracy, execution time (in
seconds) and how much instances are correctly classified or not
classified correctly.
KEYWORDS: Data Mining, J48, Random Tree, Naïve Bayes,
Multilayer Perceptron, WEKA
How to cite this paper: Sakshi Goel |
Neeraj Kumar | Saharsh Gera
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I. INTRODUCTION
Data Mining is the process of exploring the patterns
with the help of various techniques in the data
gathered from the various sources [1]. Data Mining
also involves selection of the relevant data from the
database, preprocessing of the relevant data,
transformation in the suitable form, data mining and
evaluation of the data and afterwards online updating
and visualization [1]. It is the analysis step of the
“KnowledgeDiscovery” process. The actual task of
the Data Mining is semi-self-regulating or self-
regulating investigation of the large batches of the
dataset for extracting the previously unknown,
unusual records and dependencies [1]. Knowledge
Discovery process includes various selection steps
which helps in the efficient extraction of the useful
data from the large datasets. These steps are sequential
steps and they are repeated in iterative sequential
manner until the useful information is not extracted.
Data Mining is one of the essential steps in the KDD
process [2].
Step 1: Selection Step: In the first step suitable data
for the investigation task is fetched from the database
[3]. On the basis of the extraction of suitable data
objective dataset is formed [2].
Step 2: Pre-Processing Step: In the second step the
data which is collected in the selection step is highly
concerned with problems like vagueness, missing and
irrelevant data due to magnificent size and
complexity. The above concerned problems are
molded into a form which is suitable for the data
mining techniques with the help of the different tools
used for the data mining [2].
Figure 1: Sequential Steps of KDD Process
IJTSRD50568

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
Step 3: Transformation Step: In the third step data
is molded into the form which is suitable for the
classification by performing different operations like
accumulation, induction, normalization, discretization
and construction operations for the features [2] [3].
WEKA tool is used for the research work.
Step 4: Data Mining: In the fourth step the Data
Mining techniques (algorithms) are used for drawing
out figures. Data Mining is used to analyze the dataset
[2] [3]. In this work Data Mining Classification
algorithms like J48, Random Tree, Naïve Bayes, and
Multilayer Perceptron are used for the investigation
using WEKA Machine Learning Tool.
Step 5: Interpretation/ Evaluation Step: In this step
data patterns are identified on the basis of the some
measures. To figure out and interpret the mining
results correctly users need visualization approach to
work with[2].
II. RELATED WORK
K. Ahmed, T. Jesmin, 2014, this paper proposes to
analyze accuracy of the data mining algorithms using
three testing beds which are Percentage Split method,
Training Data Set method and Cross Validation
method. The classification is performed on type-2
Diabetes disease dataset. According to this research
paper the top 5 algorithms for classifying diabetes
patients are Bagging (accuracy 85%), Logistic and
Multiclass Classifier(accuracy 81.82%) [4].
C. Anuradha, T. Velmurugan, 2015, this paper
comes up with the prediction of the future outcome of
the final year results of UG student’s dataset. Cross
fold validation and percentage split are the two testing
beds used in the classification. According to the
research Naïve Bayes and Bayes Net performs well
for the data set taken and K-NN, OneR performs
poorly [5].
S. Gupta, N. Verma, 2014, proposes to analyze the
classification algorithms on the basis of the Mean
Absolute Error, Root Mean Squared Error and the
Confusion Matrix. The performance evaluation is
being done on the Naïve Bayes classifier and
according to the research the Mean Absolute Error
and the Root Mean Squared Error is less in case of
the training data set. According to the evaluated
results Naïve Bayes comes outto be the best suited
algorithm [6].
R. Sharma et al, 2015, worked with various data
mining algorithms to comparatively analyze those
using criteria’s like definitiveness, execution time,
different datasets and their applications. The
algorithms which have been compared in the research
are M5P algorithm, K Star algorithm, M5 Rule
algorithm, Multilayer Perceptron algorithm. For the
large dataset K-star comes out with the highest
definitiveness. [7].
N. Orsu et al, 2013, stated about the different
classification algorithms and their comparisons on
micro-array of data that helps in predicting the
occurrence of the tumor. Authors have compared 14
different classification algorithms on the basis of the
accuracy. According to the research work all
classifiers comes out with the significant
performances in terms of accuracies [8].
S. Khare, S. Kashyap, 2015, provided analysis of the
different classification algorithms which includes
decision tree, bayesian network, k-nearest neighbor
classifiers and artificial neural networks. A brief
description of data mining and classification is given
in the paper. Voting Dataset is used for analysis.
According to the research work decision tree accuracy
is better than the other algorithms [9].
Md. N. Amin, Md. A. Habib, 2015, worked on the
comparative analysis of J48 decision tree, multilayer
perception, and naïve bayes. According to the authors
the research work shows the best algorithm is J48 with
an accuracy of 97.61%, and the algorithm which is
having lowest error rate with 27.91% is NaïveBayes
[10].
S. Carl et al, 2016, worked on the comparative
analysis of data mining algorithms which are k-means
algorithms, k nearest neighbor algorithm, decision tree
algorithm, naïve bayes algorithm. From the research
performed by the authors they have found that k
means algorithm have less error rate and is the easier
algorithmas compared to the KNN and Bayesian [11].
S. Vijayarani, M. Muthulakshmi, 2013, worked on
the performance analysis of the bayesian and lazy
algorithms. Various performance factors like ROC
area, Kappa Statistics, TP Rate etc are used for the
analysis. From the comparison it can be concluded
that Lazy classifiers is efficient than the Bayesian
classifiers [12].
S. Nikam, 2015, worked on the comparative analysis
of classification algorithm like C4.5, ID3, k- nearest
neighbor, Naïve Bayes, SVM and ANN. Each
algorithm has its limitations and features and based on
theconditions we can choose the best suited algorithm
for our dataset [13].
G. Raj et al, 2018, has shown comparative analysis of
the classification algorithms using WEKA on
hematological data of diabetic patients. The
algorithms which have been studied are J48 decision
tree, Zero R, Naïve Bayes. From this comparison it
can be concluded that Naïve Bayes is the best
algorithm on diabetic data with 76.3021% accuracy.

Naïve Bayes classifier can be used to enhance the
traditional classification methods which are used in
the medical or bioinformatics areas [14].
N. Jagtap et al, 2017, provided a comprehensive
analysis of different classification algorithms like
Support Vector Machines, Bayesian Networks,
Genetic Algorithms, Fuzzy Logic etc. The
comparative study of the algorithms is done on the
basis of the advantages and disadvantages of the
algorithms [15].
N. Nithya et al, 2014, stated about the Logistics,
Simple Logistics, SMO algorithms which are
compared on the basis of the accuracy measurement,
TP Rate, FP Rate, Precision, Kappa Statistics etc.
According to the analysis Logistics method suits best
from the Function Classifier Algorithm, but according
to the time accuracy SMO produces the best result
[16].
S. Chiranjibi, 2015, worked on the comparative
analysis of Naïve Bayes, Bayes Network, Logistics,
Decision tree, Multilayer Perception, REPTree,
ZeroR, Ada Boost. From the work it can be concluded
that logistic algorithm is best which works well for the
higher no of attributes and higher no of instances [17].
C. Fernandes et al, 2017, describes about the
different decision tree classifiers and the decision tree
classifiers are used to forecast student’s proficiency.
CHAID has highest accuracy rate that is
76.11%followed by C4.5 by 73.13% [18].
S. Srivastava et al, 2013, worked on the performance
of classification algorithms and results are compared
and evaluation is done on the already existing
datasets. Accuracy of the SPRINT algorithm is more
and the performance is satisfactorily good [19].
A. Lohani et al, 2016, worked on the comparative
analysis of the algorithms and the result of the
analysis is shown using ROC (Receiver Operating
System) graphically. This paper shows that if
ensemble methods are used than better results can be
seen. C4.5 algorithm is not stable [20].
S. Devi, M. Sunadaram, 2016, stated about the data
mining and the various research domains, about meta
and tree classifiers. This paper provides analysis
between meta and tree classifiers and as a result of the
analysis it is shown that meta classifier is more
efficient than tree classifier [21].
S. Priya, M. Venila, 2017, stated about the cancer
diagnosis which is a field of healthcare and the
diagnosis of the disease is done with the help of the
data mining classification algorithms on the basis of
the correctly and incorrectly classified instances [22].
K. Danjuma, A. Osofisan, 2014, stated about various
classification algorithms and they have been
comparatively analyzed using cross-fold validation
method and sets of performance metrics. The analysis
shows that 97.4% accuracy was of Naïve Bayes,
Multilayer Perceptron having 96.6% and J48 comes
with much less accuracy that is 93.5% [23].
N. Kaur, N. Dokania, 2018, worked on the
comparative analysis of k-mean and y-mean done on
the basis of the features like efficiency, number of
clusters an item belongs, performance, shape of
cluster, detectionrate etc.[24].
E. Sondakh, R. Pungus, 2017, worked on the
comparative analysis of three classification algorithms
tocompose the best suited algorithm for model. Three
algorithms resulting models shows no significant
difference between performance of Naïve Bayes and
Decision Tree while SVM shows lowest performance
[25].
K. Kishore, M. Reddy, 2017, stated about data mining
and its different techniques. Two things have been
explained one the comparison between different
datasets using one algorithm and second comparison
of different algorithms using single dataset [26].
III. RESEARCH METHODOLOGY
In data mining classification of large data set is a
problem. Data mining has various techniques like
classification, regression, clustering etc. This paper
mainly focuses on the classification techniques having
various algorithms which will help in classifying the
records. The datasets contains instances or the classes
and the attributes which helps in classifying the
records. Random Tree, J48 Decision Tree, Multilayer
Perceptron and Naïve Bayes are the algorithms used
for the analysis of the classification techniques.
The research work mainly focuses on the comparative
analysis of the classification algorithms which are
Naïve Bayes, Multilayer Perceptron, Random Tree
and J48 on Chronic Kidney Disease dataset. The
results of comparative analysis are anatomized to
deduce best suited algorithm on the basis of
definitiveness, execution time, correctly classified
instances and incorrectly classified instances.
A. DATASET USED: In this research work we
have used Chronic Kidney Disease (CKD)
dataset. The main focus of this reasearch is
performance and evaluation of Naïve Bayes,
Multilayer Perceptron, J48, Random Tree
algorithms. This dataset contains 400 instances
and 25 attributes. For analyzing the performance
of the classification algorithms WEKA data
mining tool is used.

Chronic Kidney Disease is a type of disease in which kidney losses its function over a period of month or year.
Clinical Diagnosis of the Chronic Kidney Disease is done with the help of urine and the samples of the blood as
well diagnosing the sample of the kidney tissue. Early diagnosis and detection of the disease is very important so
that failure of the kidney can be stopped. For predicting chronic kidney disease data mining and analytics
techniques are used and historical patient’s data and diagnosis records are used. Using the CKD dataset
comparative analysis of the algorithms is done on the basis of parameters accuracy, properly graded instances,
improperly graded instances, error rate and execution time [28].
Figure 2: Abbreviations used in dataset
Figure 3: Instances and Attributes in Dataset

B. CLASSIFICATION: Classification is a data mining technique and is a supervised learning having broad
applications. Classification technique classifies each item of a set into a predefined set of classes or groups.
Among all the techniques in the data mining the apex technique is classification. Dataset is being inspected
by classification and each instance of the dataset is considered. The instances which are inspected and
considered by the technique are appointed to appropriate class such that there will be least error in the model
[29].
Models defining the influential data classes inlying in a particular dataset are withdrawn using classification
technique. The two states of the classification includes application of the algorithm to construct the model and
afterwards constructed model is tested contrary to a already defined dataset to measure the performance and
definitiveness(accuracy) of the model. In this research work we have analyzed Naïve Bayes, Random Tree, J48
and Multilayer Perceptron algorithms on Chronic KidneyDisease dataset. Above algorithms are brieflydescribed
below:
NAÏVE BAYES: Naive Bayes is one of the classifier algorithms in data mining under the bayes class or it can be
said that it is an enhanced form of bayes theorem. The possible result is calculated according to the input in
Bayesian classifier. Those features of class are considered by the naïve bayes which are not related to any other
feature of the class [29]. Working of naïve bayes algorithm is described as follows:
P (d|b) Posterior probability of class (target) given predictor (attribute) of class.
P(d) Prior probability of class.
( | ) × ( )
( | ) =
( )
( | ) = ( | ) ∗ ( | ) ∗ ( | ) ∗ … … … … ∗ ( | ) ∗ ( )
Figure 4: Naïve Bayes Theorem [30]
P (b|d) likelihood which is the probability of predictor of given class.
P(b) Prior probability of predictor of class.
J48: J48 classifier is the enhanced version of the C4.5 classifier. Decision tree is produced as a resultbythe J48.
Decision tree produces a tree like structure which has different nodes in it. These different nodes in the tree
contain some judgment and each judgment leads to the particular outcome known as decision tree [10]. Simple
algorithm is being followed by the J48 which works as follows:
New items are being classified by constructing a decision tree which uses available training datasets values after
that those attributes are identified who segregates the distinct instances most clearly [30]. Due to this highest
information from the data instances can be gained [30]. Dataset is partitioned into commonly restricted areas
where each area has its own tag, values and associated actions to describe its data points. This partitioning helps
in deciding which portion of the tree is reaching to a particular resulting node [10].
MULTILAYER PERCEPTRON: Linearlyseparable problems can be classified bythe single layer perceptron.
We use more than one or multiple layers for the non-separable problems. For this we use multilayer network. The
Multilayer (feed forward) network has multiple layers including multiple hidden layers containing neurons and
these neurons are hidden neurons. By using the past data input is correctly mapped into the output when desired
output is not known. With each input the output of the neural network is compared with the desired output so as
to compute the error [10]. For computing the error output produces by the neural network is compared with the
desirable output [10].

Figure of the multilayer network is shown below:
Figure 5: Multilayer Perceptron
RANDOM TREE: Random Tree is a type of supervised learning algorithm. This learning algorithm produces
various trainees. Random Trees have been introduced by the Leio Brieman and Adele Cutler. Randomtree is a
group of tree predictors which is known as forest. The random tree algorithm is as follows: random treeclassifier
get its input feature vector, this input vector is compared with each tree in the forest and gives the name of the
class as an output with which this input vector matches having majority of votes. 2 machine algorithms are
combined to form the random forest. Random forest ideas are combined with single modeled trees.
TOOL USED: WEKA known as Waikato Environment for Knowledge Analysis which is constructed in New
Zealand in the University of Waikato. This machine learning software is written in Java. WEKA is a collection of
visualization tools and algorithms for the predictive modeling [27]. Different types of data mining algorithms can
be tested using different type of datasets. The techniques which are supported bythe WEKA are Data Processing,
Classification, Clustering, Visualization Regression and Feature Selection [21]. There are 5 interfaces in the tool
and main user interface is explorer with which we work but all other interfaces provides same functionalityjust as
the explorer [27].
IV. EXPERIMENTAL RESULTS
This research work analyses different classification algorithms accomplishment for Chronic Kidney Disease
dataset. Comparison of classifiers for Chronic Kidney Disease dataset is done using criteria accuracy, correctly
classified instances, incorrectly classified instances, error rate and execution time to analyse the performance of
the classification algorithms and its application domain is also discussed. Models for each algorithm are
constructed using two methods maily – Cross Validation with 10 folds out of which training set uses 9 folds and 1
fold for testing and Percentage Split in which 60% of the dataset is used for the training and 40% is used for the
testing and output is given according to it.
Figures are shown for the comaprison of the different classifiers for CKD dataset using 10 fold cross validation
testing bed. Applications are also discussed of these classifiers in the table. According to the table andresearch
the execution time taken by the Random Tree algorithm is least with 0.02 seconds followed by Naïve Bayes with
0.02 seconds, J48 algorithm with 0.1 seconds and multilayer perceptron took much more time for execution
which is 8.97 seconds. Accuracy of Multilayer perceptron is 99.75%, J48 with 99%, Random treewith 95.5%
and naïve Bayes with 95%. The accuarcies of the algorithms don’t have much difference in between. Hence
according to the data Multilayer perceptron algorithm is most accurate in case of 10 fold cross validation
method.

Figure 6: Result evaluation for different classification algorithm on CKD dataset
For Chronic Kidney Disease
Classifier Naïve Bayes MultilayerPerceptron Random Tree J48
Testing Bed Cross Validation Cross Validation Cross Validation
Cross
validation
Applications
Text classification,
Spam filtering,
Online Application,
Hybrid
recommendersystem
Speech recognition,
Imagerecognition,
Machine translation
software [32].
Machine learning,
Genetic algorithm,
Fault diagnosis,
Rotating
Machinery [33].
Emotion
recognition,
Verbal
column
pathologies.
ExecutionTime 0.03 seconds 8.97 seconds 0.02 seconds 0.1 seconds
Accuracy 95% 99.75% 95.5% 99%
Table 1: Comparison of classifiers for CKD dataset using cross validation testing bed
Figure 7: Graphical representation of different algorithms accuracy and execution time using cross
validation method.

In the graph the abbreviation NB stands for Naïve Bayes, MP for Multilayer Perceptron, RT for Random Tree.
The number of correctly classified instances in Naïve Bayes is 380, Multilayer perceptron with 399, Random
tree with 382 and J48 with 396. The incorrectly classified instances by Naïve Bayes is 20, Multilayer perceptron
with 1, Random tree with 18 and J48 with 4. Now analysis for CKD using percentage split method is done and
this is as below:

For Chronic Kidney Disease
Classifier NaïveBayes MultilayerPerceptron RandomTree J48
TestingBed PercentageSplit PercentageSplit PercentageSplit PercentageSplit
ExecutionTime 0 seconds 0 seconds 0 seconds 0.01 seconds
Accuracy 95% 98.125% 96.25% 100%
Tale 2: Comparison of classifiers for CKD dataset using pecrentage split method
According to this test method that is percentage split it can be concluded that Naïve Bayes, Random Tree and
Multilayer Perceptron took 0 sceonds for execution while J48 took 0.01 seconds for execution. Accuracy of the
J48 algorithm comes out to be 100% while that of Multilayer Perceptron with 98.125%, Naïve Bayes with 95%
accurate and random Tree with 96.25% accuarte. The number of correctly classified instancesin Naïve Bayes is
152, Multilayer Perceptron with 157, Random Tree with 154 and J48 with 160. Number of incorrectly classified
instances in Naïve Bayes is 8, Multilayer Perceptron with 3, Random Tree with 6 and J48 with 0.

Figure 8: Graphical representation of different algorithms accuracy and execution time in percentage
split
Graphical representation of different algorithms accuracy in percentage split method. The abbreviationsin the
chart stands for Naïve BAyes, Multilayer Perceptron, Random Tree.
Graphical representation of correctly and incorrectly classified instnces by the classifiers are:
Figure 9: correctly and incorrectly classified instances in case of Percentage Split
Figure 10: correctly and incorrectly classified instances in case of Cross Validation
From the graphs it is analyzed that there is no such difference between the perfromance of the classification
algorithms they have significant performances for the chronic kidney disease dataset but on th basis of graph
analysis Multilayer Perceptron classifier is most accurate when using cross validation method andJ48 classifier is
most accurate when using percentage split.
V. CONCLUSION
Comparision and investigation of the accomplishment
of various classification algorithms is done using
different criteria which are accuracy, execution time,
correctly classified instances, incorrectly classified
instances and error rate. According to the result
evaluation it can be concluded that Multilayer
Perceptron is most accurate with 99.75% when 10
folds cross validation method is applied for CKD
dataset and for Percentage Split method J48 algorithm
is most accurate with 100% accuracy. From the figure
7 and 8 it can be analyzed that all the algorithms don’t
have much significant difference in between their
accuracies. Hence type and size of the datasets are the
factors on which algorithms performance depends.
The further result evaluation study can be done for the
performance of other classification techniques with
large dataset sample. Clustering, association,
sequential patterns etc techniques can be used to draw
more efficient results apart from the classification
technique

VI. FUTURE WORK
In future focus will be on how to improve the
classifiers performance so that classification
techniques requiresless time to execute. For enhancing
the performance different classification algorithmscan
be used together.
REFERENCES
[1] https://en.wikipedia.org/wiki/Data_mining
[2] R. Sharma et al, “Comparative Analysis of
Classification Techniques in Data Mining Using
Different Datasets”. International Journal of
Computer Science and Mobile Computing, vol.
4, PP 125-134, No. 12(2015).
[3] https://data-flair.training/blogs/data-mining-and-
knowledge-discovery/
[4] K. Ahmed, T. Jesmin, “Comparative Analysis
of Data Mining Classification Algorithms in
Type-2 Diabetes Prediction Data Using Weka
Approach”. International Journal of Science and
Engineering, vol. 7, PP 155-160, No. 2(2014).
[5] C. Anuradaha, T. Velmurugan, “A
Comparative Analysis on the Evaluation of
Classification Algorithms in the Prediction of
Students Performance”. International Journal of
Science and Technology, vol. 8, No. 15(2015).
[6] S. Gupta, N. Verma, “Comparative Analysis
of the Classification Algorithms using Weka
Tool”. International Journal of Scientific and
Engineering Research, vol. 7, No. 8(2014).
[7] R. Sharma et al, “Comparative Analysis of
Classification Techniques in Data Mining using
Different Datasets”. International Journal of
Computer Science and Mobile Computing, vol.
4, PP 125-134, No. 12(2015).
[8] N. Orsu et al, “Performance Analysis and
Evaluation of Different Data Mining
Algorithms used for Cancer Classification”.
International Journal of Advanced Research in
Artificial Intelligence, vol. 2, PP 49-55, No.
5(2013).
[9] S. Khare, S. Kashyap, “A Comparative
Analysis of Classification Techniques on
Categorical Data inData Mining”. International
Journal on Recent and Innovation Trends in
Computing and Communication, vol. 3, PP
5142-5147, No. 8(2015).
[10] Md. N. Amin, Md. A. Habib, “Comparison of
Different Classification Techniques using
WEKA for Hematological Data”. American
Journal of Engineering Research, vol. 4, PP 55-
61, No. 3(2015).
[11] S. Carl et al, “Implementation of Classification
Algorithms and their Comparisons for
EducationalDatasets”. International Journal of
Innovative Science, Engineering and
Technology, vol. 3, PP 700-705, No. 3(2016).
[12] S. Vijayarani, M. Muthulakshmi, “Comparative
Analysis of Bayes and Lazy Classification
Algorithms”. International Journal of Advanced
Research in Computer and Communication
Engineering, vol. 2, PP 3118-3124, No.
8(2013).
[13] S. Nikam, “A Comparitive Study of
Classification Techniques in Data Mining
Algorithms”. Oriental Journal of Computer
Science and Technology, vol. 8, PP 13-19, No.
1(2015).
[14] G. Raj et al, “Comparison of Different
Classification Techniques using WEKA for
Diabetic Diagnosis”. International Journal of
Innovative Research in Computer and
Communication Engineering, vol. 6, PP 509-
516, No. 1(2018).
[15] N. Jagtap et al, “A Comparative Study of
Classification Techniques in Data Mining
Algorithms”. International Journal of Modern
Trends in Engineering and Research, vol. 4, PP
58-63, No. 10(2017).
[16] N. Nithya et al, “Comparative Analysis of
Classification Function Algorithms in Data
Mining”. International Conference on
Information and Image Processing, PP 272-275,
No. 2(2014).
[17] S. Chiranjibi, “A Comparative Study for Data
Mining Algorithms in Classification”. Journal
of Computer Science and Control Systems, vol.
8, PP 29-32, No. 1(2015).
[18] C. Fernandes, et al, “A Comparative Analysis of
Decision Tree Algorithms for Predicting
Student’s Performance”. International Journal
of Engineering Science and Computing, vol. 7,
PP 10489-10492, No.4(2017).
[19] S. Srivastava et al, “Comparative Analysis of
Decision tree Classification Algorithms”.
International Journal of Current Engineering
and Technology, vol. 3, PP 334-337, No.
2(2013).
[20] Lohani et al, “Comparative Analysis of
Classification Methods Using Privacy
Preserving Data Mining”. International Journal
of Recent Trends in Engineering and Research,
vol. 2, PP 677-682, No. 4(2016).

[21] S. Devi, M. Sundaram, “A Comparative
Analysis of Meta and Tree Classification
Algorithms Using WEKA”. International
Research Journal of Engineering and
Technology, vol. 3, PP 77-83, No. 11(2016).
[22] S. Priya, M. Venila, “A Study on
Classification Algorithms and Performance
Analysis of Data Mining Using Cancer Data
to Predict Lung Cancer Disease”.
International Journal of New technology and
Research, vol. 3, PP 88-93, No. 11(2017).
[23] K. Danjuma, A. Osofisan, “Evaluation of
Predictive Data Mining Algorithms in
Erythemato-Squamous Disease Diagnosis”.
International Journal of Computer Science
Issues, vol. 11, PP 85-94, No. 1(2014).
[24] N. Kaur, N. Dokania, “Comparative Study of
Various Techniques in Data Mining”.
International Journal of Engineering Sciences
and Research Technology, vol. 7, PP 202-209,
N0. 5(2018).
[25] E. Sondakh, R. Pungus, “Comparative Analysis
of Three Classification Algorithms in
Predicting Computer Science Students Study
Duration”. International Journal of Computer
and Information Technology, vol. 6, PP 14-18,
No. 1(2017).
[26] K. Kishore, M. Reddy, “Comparative Analysis
between Classification Algorithms and Data Set
(1: N andN: 1) Through WEKA”. Open Access
International Journal of Science and
Engineering, vol. 2, PP 23-28,No. 5(2017).
[27] https://en.wikipedia.org/wiki/Weka_(machine_l
earning)
[28] F. Aqlan, R. Markle, “Data Mining for Chronic
Kidney Disease”. Proceedings of the 2017
Industrial andSystems Engineering Conference,
vol. 4, No. 3(2017).
[29] https://data-flair.training/blogs/classification-
algorithms/
[30] https://www.google.com/search?q=naive+bayes
+theorem+formula&source=lnms&tbm=isch&s
a=X&ved=0ahUKEwjXtcSJr-
zbAhXMMY8KHbBVBK0Q_AUICigB&biw=
1366&bih=662#imgrc=kwLT20eBUyxVdM:
[31] Mishra, B. Ratha, “Study of Random Forest
Data Mining Algorithms for Microarray Data
Analysis”. International Journal on Advanced
Electrical and Computer Engineering, vol. 3, PP
5-7, No. 4(2016).
[32] https://en.wikipedia.org/wiki/Multilayer_percep
tron#Applications
[33] https://link.springer.com/chapter/10.1007/978-
1-84628-814-2_82

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