Analysis and Prediction of Diabetes Diseases using Machine Learning Algorithm: Ensemble Approach

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 10 | Oct -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 426
Analysis and prediction of diabetes diseases using machine learning
algorithm: Ensemble approach
Rahul Joshi1, Minyechil Alehegn2
1 Assistant Professor Dept. of Computer science and engineering, Symbiosis Institute of Technology,
Pune - 412115, Maharashtra, India
2Dept. of Computer science and engineering, Symbiosis Institute of Technology, Pune - 412115, Maharashtra, India
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Abstract - Machine learning techniques (MLT) are used to predict the medical datasets at an early stage of safe human life.
A huge medical datasets are accessible in different data repositories which used to in the real world application. Now a day
Machine learning (ML) has the ability to answer questions. One of the missions is a prediction on disease data. Currently
Diabetes Diseases (DD) are among the leading cause of death in the world. To group and predict symptoms in medical data,
various data mining techniques were used by different researchers in different time. A total of 768 instances, data set from
PIDD (Pima Indian Diabetes Data Set). In this system the most known predictive algorithms apply KNN, Naïve Bayes, Random
forest, and J48. By using these algorithms make an ensemble hybrid model by combining individual techniques/methods into
one in order to increase the performance and accuracy.
Key words: Ensemble, Diabetes, classification, Machine learning, Data mining, KNN, Naïve Bayes, Random Forest,
J48.
1. INTRODUCTION
Diabetes diseases commonly stated by health professionals or doctors as diabetes mellitus (DM), which describes a set of
metabolic diseases in which the person has blood sugar, either insulin production inefficient, or because of the body cell
do not return correctly to insulin, or by both reason. The day is now to prevent and diagnose diabetes in the early stages.
According to the WHO (world health organization) report in Nov 14, 2016 in the world diabetes day “Eye on diabetes”
reported 422 million adults are with diabetes, 1.6 million deaths, as the report indicates it is not difficult to guess how
much diabetes is very serious and chronic.
In 2014, 8.5% of adults whose ages are 18 and older than 18 had diabetes. In 2012 HBG (high blood glucose was the cause
of 2.2 million people deaths [53]
Diabetes diseases damage different parts of the human body from those parts some of them are: eyes, kidney, heart, and
nerves. Williams’s textbook of endocrinology was predictable that in 2013 more than 382 million population in the world or
all over the world were with diabetes or had diabetes. There are so many peoples are died every year by diabetes disease
(DD) both in poor and rich countries in the world.
According to the centers for disease control and prevention (CDCP) they give information for the duration of 9 ensuing
years that is between 2001 and 2009 type II diabetes increased 23% in the United States (US). There are different
countries, organization, and different health sectors worry about this chronic disease control and prevent before the
person death.
Diabetes. Most in the current time diabetes is grouped into two types of diabetes, type I and Type II diabetes. Type I
diabetes this type of diabetes in heath language or in doctors' language this type of diabetes also called Insulin dependent
diabetes illness. Here the human body does not produce enough insulin. 10 % of diabetes caused by this type of diabetes.
Type II diabetes this type of diabetes. According to CDA (Canadian Diabetes Association) during 10 years, between 2010
and 2020, expected to increase from 2.5 million to 3.7 million. Therefore, as the above mentioned Diabetes diseases needs
early preventation and diagnosis to safe human life from early death .By considering how much this disseises is very series
and leading one in the world. Moloud et al. [2] Algorithms which are used in machine learning have various power in both
classification and predicting.
Abdullah et al. [40] Data mining methods support health care researchers to retrieve novel knowledge from large health
data. With the development of Information Technology, Data mining offers appreciated advantage in diabetes research,

which leads to expand or improve health care distribution, increase support for decision –making and improve disease
supervision.
Saba et al. [12] no single technique gives highest accuracy or accuracy for all diseases, whereas one classifier provides or
shows better performance in a given dataset, another method or approach outdoes the others for other diseases. The new
study or the proposed study concentrates on a novel combination of different classifiers for diabetes disease (DD)
classification and prediction, thus overcoming the problem of individual or single classifiers.
This study follows different machine learning algorithms to predict diabetes disease at an early stage.Such as, KNN, Naïve
Bayes, Random Forest, and J48 to predict this chronic disease at an early stage for safe human life.
2. RELATED WORK
Song et al. [8] Describe and explain different classification Algorithms using different parameters such as Glucose, Blood
Pressure, Skin Thickness, insulin, BMI, Diabetes Pedigree, and age. The researches were not included pregnancy
parameter to predict diabetes disease (DD). In this research, the researchers were using only small sample data for
prediction of Diabetes. The algorithms were used by this paper were five different algorithms GMM, ANN, SVM, EM, and
Logistic regression. Finally. The researchers conclude that ANN (Artificial Neural Network) was providing High accuracy
for prediction of Diabetes.
Loannis et al.[7] machine learning algorithms are very important to predict different medical data sets including diabetes
diseases dataset(DDD).in this study they use support vector machines(SVM) ,Logistic Regression ,and Naïve Bayes using
10 fold cross validation to predict different/varies medical datasets including diabetes dataset(DD) .the researchers’ was
compare the accuracy and the performance of the algorithm based on their result and the researchers conclude that
SVM(support Vector Machine ) algorithm provides best accuracy than the other algorithm which are mentioned on the
above . The researchers were use those machine learning algorithm on a small sample of data.in this study factors for
accuracy were identified such factors are Data origin, Kind, and dimensionality.
Nilashi et al. [9] .CART (classification and Regression Tree) was used for generating fuzzy rule. Clustering algorithm also
was used (principal component Analysis (PCA) and Expectation maximization (EM) for pre-processing and noise removing
before applying the rule. Different medical dataset (MD) was used such as breast cancer, Heart, and Diabetes Develop
decision support for different diseases including diabetes. The result was CART (Classification and Regression tree) with
noise removal can provide effective and better in health/diseases prediction and it is possible to safe human life from early
death.
Yunsheng et al. [1] this study was the new approach that used KNN algorithm by removing the outlier/OOB(out of bag)
using DISKR(decrease the size of the training set for K-nearest neighbour .and also in this study the storage space was
minimized. There for ,the space complexity is become less and efficient .after removing a parameters or instances which
have less effect or factor the researchers got better accuracy .
Francesco et al.[4]feature selection is one of the most important step to increase the accuracy.Hoeffding Tree(HT) ,multi-
layer perceptron(MP),Jrip,BayeNet,RF(random forest),and Decision Tree machine learning Algorithms were used for
prediction .From different feature selection algorithm in this study they were use best first and greedy stepwise feature
selection algorithm for feature selection purpose . The researchers conclude that Hoeffding Tree (HT) provides high
accuracy.
Pradeep et al.[29]in this study the researchers concentrate on different datasets including Diabetes Dataset(DD).The
researcher were investigate and construct the models that are universally good and capability for varies/different medical
datasets (MDs).the classification algorithm did not evaluate using Cross validation evaluation method .
ANN,KNN,Navie Bayes,J48,ZeroR,Cv Parameter selection, filtered classifier ,and simple cart were some of the algorithm
used in this study. From those algorithm Naïve Bayes provide better accuracy in diabetes dataset (DD) in this study. The
two algorithm KNN and ANN provide high accuracy in other datasets on this study.
Sajida et al.[16]by using CPCSSN(Canadian primary care sentinel surveillance Network ) dataset and three machine
learning methods to predict the diabetes Disses (DD) in early stage to safe human life at from early death .on this study
Bagging ,Adaboost,and decision tree(J48) were used to predict the diabetes and the researcher was compare the result of
those methods and concluded that Adaboost method was provide effective and better accuracy than the other methods in
weka data mining tools

Kamadi et al. [17] classification problems were identified in this study.one of the most problem in classification is data
reduction .it has a vital role in prediction accuracy .to get better and efficient accuracy the data should be reduced as the
researchers studied here. On this study PCA (principal component Analysis) for data pre-processing including data
reduction for better accuracy. For prediction modified decision tree (DT) and Fuzzy were used for prediction purpose
.finally it was concluded as to get better result the dataset should be reduced.
Pradeep & Dr.Naveen [15] in this study the performance of machine learning techniques were compared and measured
based on their accuracy. The accuracy of the technique is vary from before pre-processing and after pre-processing as they
identified on this study. This indicates the in the prediction of diseases the pre-processing of data set has its own impact on
on the performance and accuracy of the prediction
Decision tree technique provide better accuracy in this study before pre-processing to predict diabetes diseases. Random
forest and support vector machine provides better prediction after pre-processing in this study using diabetes data set.
Santhanam and Padmavathi [21]K-means and Genetic algorithm used in this study for Dimension reduction in order to get
better performance. The integration of support vector machine for prediction technique was used and provide better
accuracy in small sample diabetes data set by selecting only five factors or parameters. 10 cross validation on this study
used as evaluation method.finaly reduced data set provide better performance than large dataset.
Xue-Hui Meng et al. [42] in this study the researchers were use different data mining techniques to predict the diabetic
diseases using real world data sets by collecting information by distributed questioner .in this study SPSS and weka tools
were used for data analysis and prediction respectively .in this study the researchers compare three techniques ANN,
Logistic regression, and j48 .finally it was concluded as j48 machine learning technique provide efficient and better
accuracy.
Abdullah et al. [40] Oracle Data miner and Oracle Database 10g used for Analysis and storage respectively .the parameters
or factors were identified in this study .the target variables were identified based on their percentage .this study
concentrated on the treatment of the patient .the patient divided into two categories old and young based on their age and
predict their treatment .for both young and old diet controle indicates high percentage on this study. The treatment
predictive percentage done by support vector machine.
3. METHODOLOGY
In diabetic disease there were different research were done .previously there were many researchers did different
researches in health care centres. From those researchers money of them also did on diabetes disease as it was series
issues in the old aged research done only on the health centres not in the computerised like machine learning approach .it
is also true now a day summary of common or major findings are given as follow in the form of table.
Table I: Summary of major findings or discoveries of diabetes prediction methodologies
Sn Authors Methodologies Findings
1 Weifeng Xu et
al.[6]
Naïv Bayes
Random forest
ID3
Adaboost
Random forest was better than other. ID3
was provided less accuracy than others.
2 Messan et al.[8] ANN,GMM,SVM,
Logistic Regression, and
ELM
ANN was best accuracy relative to others.
3 Loannis et al.[7] Logistic regression
Naïve Bayes
Svm
In this study svm with accuracy of 84% with
10 fold cross validation
4 Mehrbakhsh et
al.[9]
CART,clustering
Algorithm(PCA and EM)
Some fuzzy rules were generated by CART.
Fuzzy rule based ,and CART by removing
noise was effective in prediction purpose
5 Tao et al.[3] KNN,Naïve Bayes, Random
Forest, decision tree, svm,
and logistic regression ,
Filtering criteria was improved. The
accuracy of recall was better in this study.

6 Yunsheng et al.[1] KNN,DISKR In this study the storage space was reduced,
an instance which have less factor was
eliminated. Removing of outlier increase
accuracy.
7 Francesco et al.[4] Hoeffding,j48,multilayer
perceptron,Jrip,Bayenet,
,Best first ,Greedy stepwise
, and Random Forest
In this study feature selection was the main
targeted. 10 fold cross validation was used
for splitting mechanism Hoeffding was
provide better accuracy by integrating with
searching algorithm with 77.5% than others.
8 Swarupa et al.[14] Naïve Bayes
ANN,KNN,J48,zeroR,cv
parameter selection ,simple
cart, and Filtered classifier
In this paper different dataset applied
including diabetes In this study any cross
validation technique was not applied. Naive
Bayes was provide high accuracy with the
accuracy value of 77.01%.
9 Sajida et al.[16] Bagging,Adaboost,and j48 In this study the researchers have got
Adaboost as the better accuracy relative to
others.
10 Munaza
Ramzan[19]
Naïve Bayes,Random
Forest,and J48
Random forest was provided better accuracy
than J48 and Naïve Bayes in 10 cross
validation splitting method.
11 Kamadi et al.[17] Modified fuzzy and PCA Data reduction was applied in this study.to
got the better accuracy the data should
reduce
12 Pradeep &
Dr.Naveen [15]
J48 It was one of most popular and noted as
better accuracy in this study .feature
selection was applied.
13 Ramiro et al.[5] Fuzzy rule In this study recommended system was
developed, it was help to reduce the wrong
treatment.
14 Pradeep et al.[29] J48,KNN,Random Forest
,and SVM
The algorithm were compared and j48 was
provided better accuracy by providing
73.82% than others before pre-processing
.KNN and RF were provided good accuracy
after pre-processing .
15 Santhanam and
Padmavathi[21]
K-means,Genetic Algorithm
,and SVM
New integrated system clustering and
classification algorithm and shown high
accuracy.
16 Sankarana &Dr
Pramananda[37]
Association rule using
apriori and FP growth.
Fast and better clinical decision making
helps for preventive and suggestive medicine
Fp growth was more advantages over
apriori
17 Xue-Hui Men et
al.[42]
J48,Logistic Regression, and
KNN
There were comparison between the
algorithms performance and j48 shown high
accuracy with 78.27%.
18 Abdullah et al.[40] SVM This study concentrated on the effective
treatment prediction.
19 Patil et al.[47] HPM It was efficient and better accuracy by
providing 92.38%
20 Saba et al.[12] HMV,NB,Adaboost,RF
SVM,KNN,and LR
Was concentrated on different diseases
including diabetes .HMV were provided high
accuracy than others with the accuracy of
78.085
21 Amit and Pragati
[30]
C4.5,RF,MLP,and Bayes Net Hybrid model was applied. From the
algorithm the hybrid of MLP+BayesNet
provided high accuracy of 81.89%
22 Saba et al.[35] ID3,C4.5 ,Bagging ,and Bagging was shown high accuracy than other

CART techniques.
23 Mounika et al.[32] ZeroR,oneR,and Naïve
Bayes
Effective treatment in young and old patient
were studied. Naive Bayes was better
performance than others
24 Nongyao and
Rungruttikarn[33]
LR, NB, ANN, Bagging,
Boosting, and Decision tree.
Hybrid concept was apply by using bagging
or boosting .RF provided high accuracy of
85.558
25 Dr Saravana et
al.[31]
Predictive analysis
algorithm in Hadoop
Concentrated on treatment in health care
industry using big data analysis. The result
shown that proper treatment with low cost
26 Veena and
Anjali[23]
SVM,NB,Decision Stump,
and decision tree
Hybridization concept was done on this
study using the base classifier with bagging
.Decision stump with provided better
accuracy of 80.72%
27 Kung et al.[34] Novel EM method ,oposit
sign test, and KNN
New and effective feature selection
mechanism done on this study by hybridizing
EM and KNN.
28 Saravananatha
n and
velmurugan[18]
J48,CART,SVM,and
KNN
In this study j48, cart, svm and knn was
applied and provide 67.15%, 62.28, 65.04
and 53.39 respectively.
29 Seokho et al.[28] SVM,E2_SVM This study was concentrated on drug failure
prediction .this study was good and
ensemble approach. E2_SVM was shown
better accuracy than single Svm with
accuracy of 80 %.
30 Rian and
Irwansyah[27]
Fuzzy rule Rules were generated in this study that were
helps early detection.
31 Yang et al.[43] Naïve Bayes, Bayes
network.
Bays network was provided high accuracy of
72.3%
32 Lin[39] SVM,ANN,Naïve Bayes, Weighted Adjusted based study. The
majority voting was applied in this study.
The combination of the classifier were
provide better accuracy than the single one
33 Vrushali and
Rakhi[10]
CLAT Prediction and severity estimation of
diabetes in different bodies were done.
34 Emrana et al.[11] C4.5 and KNN In this study c4.5 and knn technique were
provided with accuracy of 90.43 and 76.96 %
respectively
35 Nahla et al[46] SVM with rule extraction
with SQRex-SVM
In this stud the combined model provided
high accuracy.
36 Kamadi et al.[38] Decision Tree, Gini index,
Gaussian fuzzy function
Decision tree model was provided better
accuracy
37 Sakorn[13] Expert system with fuzzy
rule
In this paper expert system for treatment
was done.
38 Ayush and
Divya[24]
CART This algorithm was provided accuracy of
75%
39 Jae et al.[26] Wrapper and linear
forward selection
The computation time was reduced in this
study.
40 Bum et al.[36] Logistic regression and
Naïve Bayes,
Anthropometry
It was focused on prediction of Fasting
Glucose Level. Here the better accuracy was
74.1%
41 Asma [45] Decision tree Decision tree was provided good result with
the accuracy of 78.1768%
42 Anjli and
Varun[20]
SVM In this study feature selection was done
using wrapper and ranker .SVM shown
accuracy of 72% with ranker feature

selection. Percentage split was applied.
43 Aruna and
Nazneen[25]
KNN, fuzzy rule, and GA In this study association between KNN and
GA were done. Some rule was generated.
44 Prajwala[22] RF and DT RF was provided good accuracy than DT
.execution time for RF was more than DT in
this study.
45 Emirhan et al.[44] ANFIS, Rough Set In this work ANFIS was provide better result
than Rough Set .
46 Krati et al.[48] KNN was gotten the accuracy of 70% in data tes1
and 57% in data test2 respectively
47 Anuja and
Chitra[41]
SVM Svm was provided the accuracy of 78%
48 Thirumal et
al.[49]
Naïve Bayes,SVM,KNN,C4.5 In this study c4.5 was shown better than
other with accuracy of 78.2552%
3.1 Data pre-processing Methods
The data that we used must be wisely composed, joined/integrated and ready for analysis [42].
The dataset used in this study obtained from public UCI repository PIDD (Pima Indian Diabetes Database) which is
available online .we will use this online available dataset for analysis and prediction of diabetes diseases. This diabetes
dataset consists 768 records and 8 attributes with one target class.in this study Weka 3.8.1 and java using netbean 8.2 use
for analysis, classification, and prediction. And also, Ensemble hybrid model with base learner for prediction is include.
3.2 Classification and prediction Methods
In this study, the following parameters are used as input pregnancies, Glucose, Blood Pressure, skin thickness, insulin,
BMI, Diabetes pedigree Function, and Age. There are a number of machine learning and statistical techniques that can used
to predict diabetes diseases. Based on the extent literature, we settled on employing four most known machine learning
algorithm (Random Forest (RF), KNN, Naïve Bayes, and J48) classification algorithm and ensemble/combined them in to
one using base learner. The following section describes these Classification techniques and their unique requirements
used in this research study.
Random forest (RF)
RF is one of the popular and adaptable algorithm used in ensemble technique .it is the best and popular machine learning
algorithm in the concept of hybrid model for the improvement performance and prediction accuracy.RF is easy to handle
large data and high dimensionality. The samples are selected arbitrarily.
KNN
K-Nearest Neighbour algorithm is one of the classification algorithm .it is the simplest and easy than other data mining
techniques .this technique classifies new belongings based on similarity measure [18].the value of k always assign positive
integer number .In this algorithm the training data are stored .based on the neighbours or nearest prediction of test data
is complete
Step/phase I. Determine k which is the number of nearby neighbours.
Step II/phase. Estimate distance between the instance and training samples.
Step/phase III: The remoteness of the training samples are sorted and the closest neighbour based on the minimum the
distance is determined in this step.
Step/phase IV: in this step we get all the classes of all the training data
Step/phase V: use the majority of the class of closest neighbours as the prediction value of the query instance.
Naïve Bayes (NB)
Naïve Bayes (NB) is one of the most popular and suitable when the imputes is large .this machine learning method

or technique need a short time complexity or computational time. NB computes based on possibility by using Bayes
formula [19].
J48
It is an improvement of ID3 classification algorithm .j48 has the ability of select a specific parameters or instances and lost
attribute. This type of classification algorithm has the ability to support continuous as well as categorical instances in the
process of tree construction rules which are constructed by this algorithm are easy and simple to understand [47].
Hybrid model
In prediction individual classification algorithms are not provided result so, it is better to make the result of those
individual classifier in to one by combining the prediction of individual classifier.an ensemble approach the problem or
limitation of distinct classifiers to increases the accuracy by combining in to one. [12, 47].hybrid model provides best
performance and accuracy than the single one that is the reason why this method chosen in this study.
Fig1:- Detail Architecture of work flow
OBJECTIVE OF STUDY
The main goal of this analysis study is predict the diabetes disease and compare the algorithm which algorithm provide
high accuracy .finally select the best algorithm to predict the diabetes disease at early stage. Examine how patients’
characteristics as well as measurements disturb diabetes cases.
4. CONCLUSION
Various data mining techniques and its application were studied or reviewed .application of machine learning algorithm
were applied in different medical data sets Machine learning methods have different power in different data set. Single
algorithm provided less accuracy than ensemble one.in most study decision tree provided high accuracy.in this study
hybrid system Weka and java are the tools to predict diabetes dataset.
Dataset(PIDD)
Training set
missing value
noisy data
duplicate removing
Random Forest Decision tree(J48)KNNNaiveBayes
Compare individual prediction value
Random Forest prediction NaiveBayes prediction KNN prediction J48 prediction
Meta classifier(Stacking or voting)
Final prediction
Classification Algorithm
Data pre-processing
Classifier training
Evaluation by
Accuracy
F-measure
Recall
Precision
Testing set
Conclusion and feature work
Validation methood
10 fold coross validation
percentage splite
New Data

ACKNOWLEDGEMENT
First of all I would like to thank the Almighty God and his mother merry Mariam for their unending blessings. I would like
to express my great attitude to my Research guide professor Rahul Joshi deep regard for his model guidance, feedback,
suggestion and constant encouragement. And also I would like to express attitude to the reviewer of the paper and their
value able suggestion. Finally I would like to thank to my friends and parents for their support.
BIOGRAPHIES
Minyechil Alehegn is currently M.Tech Candidate in the department of computer Science and
Engineering Symbiosis Institute of Technology, India. He is Received his B.SC. Degree in Information
Technology from Wollega University, Ethiopia .He worked at Mizan Tepi University from 2014 to 2015 as
lecturer. His research interest include Machine learning, Data mining, NLP, and Artificial Intelligence.
REFERENCES
[1] Song, Y., Liang, J., Lu, J., & Zhao, X. (2017). An efficient instance selection algorithm for k nearest neighbour
regression. Neurocomputing, 251, 26-34.
[2] Abdar, M., Zomorodi-Moghadam, M., Das, R., & Ting, I. H. (2017). Performance analysis of classification algorithms on
early detection of liver disease. Expert Systems with Applications, 67, 239-251.
[3] Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., ... & Chen, Y. (2017). A machine learning-based framework to identify
type 2 diabetes through electronic health records. International journal of medical informatics, 97, 120-127.
[4] Mercaldo, F., Nardone, V., & Santone, A. (2017). Diabetes Mellitus Affected Patients Classification and Diagnosis
through Machine Learning Techniques. Procedia Computer Science, 112(C), 2519-2528.
[5] Meza-Palacios, R., Aguilar-Lasserre, A. A., Ureña-Bogarín, E. L., Vázquez-Rodríguez, C. F., Posada-Gómez, R., & Trujillo-
Mata, A. (2017). Development of a fuzzy expert system for the nephropathy control assessment in patients with type 2
diabetes mellitus. Expert Systems with Applications, 72, 335-343.
[6] Xu, W., Zhang, J., Zhang, Q., & Wei, X. (2017, February). Risk prediction of type II diabetes based on random forest
model. In Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2017 Third
International Conference on (pp. 382-386). IEEE.
[7] Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data
mining methods in diabetes research. Computational and structural biotechnology journal.
[8] Komi, M., Li, J., Zhai, Y., & Zhang, X. (2017, June). Application of data mining methods in diabetes prediction. In Image,
Vision and Computing (ICIVC), 2017 2nd International Conference on (pp. 1006-1010). IEEE.
[9] Nilashi, M., bin Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017). An Analytical Method for Diseases Prediction Using
Machine Learning Techniques. Computers & Chemical Engineering.
[10] Balpande, V. R., & Wajgi, R. D. (2017, February). Prediction and severity estimation of diabetes using data mining
technique. In Innovative Mechanisms for Industry Applications (ICIMIA), 2017 International Conference on (pp. 576-
580). IEEE.
Rahul Joshi is presently pursuing PhD at Symbiosis Institute of Technology, India till now. He is received
M.Tech from IIT, Mumbai, India. He worked at Symbiosis Institute of Technology as Assistant Professor
.He Worked as a Software Developer in ASCIPL, Mumbai from June 2010 to May 2011. His research
interest include Machine learning, Data mining, Networking, NLP, Big Data, and Artificial Intelligence.

[11] Hashi, E. K., Zaman, M. S. U., & Hasan, M. R. (2017, February). An expert clinical decision support system to predict
disease using classification techniques. In Electrical, Computer and Communication Engineering (ECCE), International
Conference on(pp. 396-400). IEEE.
[12] Bashir, S., Qamar, U., Khan, F. H., & Naseem, L. (2016). HMV: a medical decision support framework using multi-layer
classifiers for disease prediction. Journal of Computational Science, 13, 10-25.
[13] Mekruksavanich, S. (2016, August). Medical expert system based ontology for diabetes disease diagnosis. In Software
Engineering and Service Science (ICSESS), 2016 7th IEEE International Conference on (pp. 383-389). IEEE.
[14] Rani, A. S., & Jyothi, S. (2016, March). Performance analysis of classification algorithms under different datasets.
In Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on (pp. 1584-
1589). IEEE.
[15] Pradeep, K. R., & Naveen, N. C. (2016, December). Predictive analysis of diabetes using J48 algorithm of classification
techniques. In Contemporary Computing and Informatics (IC3I), 2016 2nd International Conference on (pp. 347-352).
IEEE.
[16] Perveen, S., Shahbaz, M., Guergachi, A., & Keshavjee, K. (2016). Performance analysis of data mining classification
techniques to predict diabetes. Procedia Computer Science, 82, 115-121.
[17] Kamadi, V. V., Allam, A. R., & Thummala, S. M. (2016). A computational intelligence technique for the effective
diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree
approach. Applied Soft Computing, 49, 137-145.
[18] Saravananathan, K., & Velmurugan, T. (2016). Analyzing Diabetic Data using Classification Algorithms in Data
Mining. Indian Journal of Science and Technology, 9(43).
[19] Ramzan, M. (2016, August). Comparing and evaluating the performance of WEKA classifiers on critical diseases.
In Information Processing (IICIP), 2016 1st India International Conference on (pp. 1-4). IEEE.
[20] Negi, A., & Jaiswal, V. (2016, December). A first attempt to develop a diabetes prediction method based on different
global datasets. In Parallel, Distributed and Grid Computing (PDGC), 2016 Fourth International Conference on (pp.
237-241). IEEE.
[21] Santhanam, T., & Padmavathi, M. S. (2015). Application of K-means and genetic algorithms for dimension reduction by
integrating SVM for diabetes diagnosis. Procedia Computer Science, 47, 76-83.
[22] Prajwala, T. R. (2015). A comparative study on decision tree and random forest using R tool. International journal of
advanced research in computer and communication engineering, 4, 196-1.
[23] Vijayan, V. V., & Anjali, C. (2015, December). Prediction and diagnosis of diabetes mellitus—A machine learning
approach. In Intelligent Computational Systems (RAICS), 2015 IEEE Recent Advances in (pp. 122-127). IEEE.
[24] Anand, A., & Shakti, D. (2015, September). Prediction of diabetes based on personal lifestyle indicators. In Next
Generation Computing Technologies (NGCT), 2015 1st International Conference on (pp. 673-676). IEEE.
[25] Pavate, A., & Ansari, N. (2015, September). Risk Prediction of Disease Complications in Type 2 Diabetes Patients Using
Soft Computing Techniques. In Advances in Computing and Communications (ICACC), 2015 Fifth International
Conference on(pp. 371-375). IEEE.
[26] Nam, J. H., Kim, J., & Choi, H. G. (2015). Developing statistical diagnosis model by discovering principal parameters for
Type 2 diabetes mellitus: a case for Korea. Public Health Prev. Med, 1(3), 86-93.
[27] Lukmanto, R. B., & Irwansyah, E. (2015). The Early Detection of Diabetes Mellitus (DM) Using Fuzzy Hierarchical
Model. Procedia Computer Science, 59, 312-319.
[28] Kang, S., Kang, P., Ko, T., Cho, S., Rhee, S. J., & Yu, K. S. (2015). An efficient and effective ensemble of support vector
machines for anti-diabetic drug failure prediction. Expert Systems with Applications, 42(9), 4265-4273.
[29] Kandhasamy, J. P., & Balamurali, S. (2015). Performance analysis of classifier models to predict diabetes
mellitus. Procedia Computer Science, 47, 45-51.

[30] kumar Dewangan, A., & Agrawal, P. (2015). Classification of Diabetes Mellitus Using Machine Learning
Techniques. International Journal of Engineering and Applied Sciences, 2(5), 145-148.
[31] Eswari, T., Sampath, P., & Lavanya, S. (2015). Predictive methodology for diabetic data analysis in big data. Procedia
Computer Science, 50, 203-208.
[32] Mounika, M., Suganya, S. D., Vijayashanthi, B., & Anand, S. K. (2015). Predictive analysis of diabetic treatment using
classification algorithm. IJCSIT, 6, 2502-2505.
[33] Nai-arun, N., & Moungmai, R. (2015). Comparison of classifiers for the risk of diabetes prediction. Procedia Computer
Science, 69, 132-142.
[34] Wang, K. J., Adrian, A. M., Chen, K. H., & Wang, K. M. (2015). An improved electromagnetism-like mechanism algorithm
and its application to the prediction of diabetes mellitus. Journal of biomedical informatics, 54, 220-229.
[35] Bashir, S., Qamar, U., Khan, F. H., & Javed, M. Y. (2014, December). An Efficient Rule-Based Classification of Diabetes
Using ID3, C4. 5, & CART Ensembles. In Frontiers of Information Technology (FIT), 2014 12th International
Conference on (pp. 226-231). IEEE.
[36] Lee, B. J., Ku, B., Nam, J., Pham, D. D., & Kim, J. Y. (2014). Prediction of fasting plasma glucose status using
anthropometric measures for diagnosing type 2 diabetes. IEEE journal of biomedical and health informatics, 18(2),
555-561.
[37] Sankaranarayanan, S. (2014, March). Diabetic prognosis through Data Mining Methods and Techniques. In Intelligent
Computing Applications (ICICA), 2014 International Conference on (pp. 162-166). IEEE.
[38] Varma, K. V., Rao, A. A., Lakshmi, T. S. M., & Rao, P. N. (2014). A computational intelligence approach for a better
diagnosis of diabetic patients. Computers & Electrical Engineering, 40(5), 1758-1765.
[39] Li, L. (2014, November). Diagnosis of Diabetes Using a Weight-Adjusted Voting Approach. In Bioinformatics and
Bioengineering (BIBE), 2014 IEEE International Conference on (pp. 320-324). IEEE.
[40] Aljumah, A. A., Ahamad, M. G., & Siddiqui, M. K. (2013). Application of data mining: Diabetes health care in young and
old patients. Journal of King Saud University-Computer and Information Sciences, 25(2), 127-136.
[41] Kumari, V. A., & Chitra, R. (2013). Classification of diabetes disease using support vector machine. International
Journal of Engineering Research and Applications, 3(2), 1797-1801.
[42] Meng, X. H., Huang, Y. X., Rao, D. P., Zhang, Q., & Liu, Q. (2013). Comparison of three data mining models for predicting
diabetes or prediabetes by risk factors. The Kaohsiung journal of medical sciences, 29(2), 93-99.
[43] Guo, Y., Bai, G., & Hu, Y. (2012, December). Using bayes network for prediction of type-2 diabetes. In Internet
Technology And Secured Transactions, 2012 International Conference for (pp. 471-472). IEEE.
[44] Yıldırım, E. G., Karahoca, A., & Uçar, T. (2011). Dosage planning for diabetes patients using data mining
methods. Procedia Computer Science, 3, 1374-1380.
[45] Al Jarullah, A. A. (2011, April). Decision tree discovery for the diagnosis of type II diabetes. In Innovations in
Information Technology (IIT), 2011 International Conference on (pp. 303-307). IEEE.
[46] Barakat, N., Bradley, A. P., & Barakat, M. N. H. (2010). Intelligible support vector machines for diagnosis of diabetes
mellitus. IEEE transactions on information technology in biomedicine, 14(4), 1114-1120.
[47] Patil, B. M., Joshi, R. C., & Toshniwal, D. (2010). Hybrid prediction model for type-2 diabetic patients. Expert systems
with applications, 37(12), 8102-8108.//19
[48] Krati Saxena, D., Khan, Z., & Singh, S.(2014) Diagnosis of Diabetes Mellitus using K Nearest Neighbor Algorithm.
[49] Thirumal, P. C., & Nagarajan, N. (2015). Utilization of data mining techniques for diagnosis of diabetes mellitus-a case
study. ARPN Journal of Engineering and Applied Science, 10(1).
[50] Chandna, D. (2014). Diagnosis of heart disease using data mining algorithm. (IJCSIT) International Journal of
Computer Science and Information Technologies, 5(2), 1678-1680.

[51] Khemphila, A., & Boonjing, V. (2010, October). Comparing performances of logistic regression, decision trees, and
neural networks for classifying heart disease patients. In Computer Information Systems and Industrial Management
Applications (CISIM), 2010 International Conference on (pp. 193-198). IEEE.
[52] Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of
heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255.
[53] http://www.who.int/mediacentre/factsheets/fs312/en/
[54] https://www.kaggle.com/uciml/pima-indians-diabetes-database

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