IRJET- Survey on Risk Estimation of Chronic Disease using Machine Learning
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This document summarizes research on using machine learning to predict chronic disease risk. It discusses how healthcare generates massive amounts of data that can be used for prediction. The paper proposes a new convolutional neural network (CNN) based model that uses both structured and unstructured data from hospitals to predict disease risk. It compares this multimodal approach to existing unimodal prediction models. The document also reviews several other studies applying machine learning to tasks like heart disease prediction using large healthcare datasets. The goal is to develop effective machine learning models for predicting disease outbreaks in communities using real hospital data.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2427
TABLE - 2: FACTORS OF HEART DISEASE
2. LITERATURE REVIEW
In [1] author has presented the concept namely, “Disease
prediction using Machine Learning over Big Data”. The big
data is fastest concept in current trend, so this concept is
applied in more fields. The big data is most widely used in
each every field because it is very large. The big data is
applied in medical field both side developing the better
growth in both fields, that is big data is applied in medical
fields develops the medical fields at the same time increase
the growth in big data field. The big data helps to achievethe
better growth in medical and health care sectors. It
additionally, provides the moremeritsgives,(i)medical data
analysis with accuracy, (ii) early prediction for disease, (iii)
patient oriented data with accuracy, (iv) The medical data,is
securely stored and used in many places, (v) incomplete
regional data are reduced and give the accuracy result. Goal
of the concept is to choose the region and collects the
hospital data or medical data of particular selected region,
this process is using the machine learning algorithm. This
term based on the data mining technique is used for disease
prediction with accuracy. Then, finding the missing data
based on latent factor to access the incomplete data and it is
reduced. The previous system use the CNNUDRP (Unimodal
Disease Risk Prediction), then continuously implements the
next level use the CNN-MDRP (Mulimodal Disease Risk
Prediction). The CNN-MDRP is overcome the drawback of
CNN-UDRP.
The CNN-MDRP consists of the hospital data, that is
structured and unstructured data. The CNNMDRPalgorithm
based prediction is more accurate,thisaccuracyiscompared
with previous system. The advantages of the concept is,
better feature description and better accuracy, and the
disadvantages of this system is, this feature is only
applicable for the structured data so it is not good in disease
description. Authors, In [2] have proposed in to the concept
is machine learning based disease prediction using the big
data for overcome the machine learning drawbacks. The
smooth progress of big data is moves in the biomedical and
healthcare communities in hospital for accurate results in
any experiment result. This concept is (a) reduces the
incomplete data and (b) effective disease prediction.
In [3] author has presentedthedata miningconcept“Disease
Prediction by using Machine Learning”.Thedata mining best
growth of the stage is develops that technique into the
healthcare basis, the data analysis is an important part of
every field. The data mining is predicts the information for
healthcare is called rapid growth of medical care field. The
existing one is designed the purpose of (i) analyze, (ii)
manage, (iii) predict of healthcare data, it is described the
overall healthcare systems. The concept ofmachinelearning
is applied into the disease-relatedinformationretrievalsand
the treatment processes in these types of process are
achieved by using the data analysis. The predictions of
outbreaks in diseases are using the decision tree, because it
is very effective. Thisconceptbasedexperimental showsthat
result is related to the disease symptoms, so that data is
described medical data using modified prediction model. If
the concept choose the raining set like medical patient
symptoms, than, use thedecisiontree,then,predicted,finally
give the symptoms of patient and get the accurate result for
disease prediction. This concept is only performs, that is
predicts only the patient related information with low time
and low cost. Authors, presents In [4] for “prediction of
disease using machine learning over big data”. Can develop
the medical specialty basis this conceptisappliedtoproduce
the medical data in to mass medical data, which means the
data which is enlarged. The goal of this concept is targeted
the simplest data is stored into the space of medical massive
data analysis, called “medical data analysis in massive
collection”. It produces the accuracy and it reaches the 4.8%
speed faster the CNN-UDRP. It only focuses this three data,
(a) structured data, (b) text data, (c) structured and text
data. In this proposed system is improves the medical data
oriented term.
Concept presented by author, In [5] delivered theme is,
“personalized disease prediction care from harm using big
data”, for healthcare analysis. This concept describes the
medical field is a rich data industry because it holds the
healthcare records, also. The daily treatment records are
increased in every day that is it includes number of
transactions, and the patient information is stored and
retrieved from the database. The medical treatment records
are every day updated one, because every day improves the
patient health improvementsbasedontreatment.Itgivesthe
correct solutions for different types of diseases. This system
is change medical record, which means manually noted
every medical oriented record intotheelectronicrecordthat
is, digitalize the medical care. This technology is simply](https://image.slidesharecdn.com/irjet-v6i6512-191209060819/85/IRJET-Survey-on-Risk-Estimation-of-Chronic-Disease-using-Machine-Learning-2-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2428
called, “e-healthcare”. The medical data is stored in the
database. The big data methods and the logics are used to
analyze the statistical analytics. The proposed system is
known as, “disease recommendation system”, and this
system holds the specialized tool, this tool is creating the
profile. The profile making needs someinformationfromthe
personalized persons, that is doctors, patients, etc, If
entering the required field of the system finally get the
personalized model health profile, but this personalization
includes huge number of profiling information and other
data. Enthusiastically presents the author, give the
information are collected by In [6] namely, “Use the
Weighted Ensemble to Neural Network based Multimodal
Disease Risk Prediction (WENNMDRP) andfeatureselection
of Ant colony improved classifier for disease predictionover
the big data concepts”. This concept feature selection
performance is gives the dataset, this data set making is one
the significant task. The feature selection task is splits into
level by level, (i) first, structured the normal and more
explainable models, (ii) apply the concept knowledge and
learning its performance, (iii) finally, ready to preparing the
clean, that is clear the data. Then, the proposed concept is
analyzes the feature selection difficulties for big data based
data analytics, so resolving this complexity by using the
Improved Ant Colony Optimization (IACO) technique. This
technique is early solves the missing data problem in
incomplete data, which means it before set the latent factor
mode, also. But is not easily selects the best feature from the
medical data.
Table 3 Comparison table
3. CONCLUSION
In the survey discuss Disease predicts the hospital data by
using the different data mining technique. This analyze the
medical data in multiple ways, like that, multidimensional
ways and view based collects that data and it escapes the
hard risks then, prediction is easily completed. The hospital
data is classified in to structureddata andunstructureddata.
The concept fulfill the existing system focused both types of
data prediction in medical area, that is big data analytics.
There are numerous researches from various domains are
continuouslyworkingtowardsdevelopingAchievingDisease
Prediction. The aim of this survey was to Summarize the
recent researches and its demerits towards achieve Disease
Prediction. This paper gives the merits and demerits of the
recent techniques and its capabilitiesarestudied.Thispaper
concludes that there is no effective method discovers for
Achieving Disease Prediction. So, further approachesshould
overcome all the above issues. Further implementation has
to be done in order to Achieving High Disease Prediction
using machine learning algorithm.
REFERENCES
[1]. Shraddha Subhash Shirsath, Prof. Shubhangi Patil
Disease Prediction Using Machine Learn.Over Big Data”. I
international Journal of Innovative Research in Science,
Engineering and Technology, [2018]. ISSN (Online) : 2319-
8753, ISSN (Print) : 2347-6710.
[2]. Vinitha S, Sweetlin S, Vinusha H, Sajini S. “Disease
Prediction Using Machine Learning Over Big Data”.
Computer Science & Engineering: An International Journal
(CSEIJ), Vol.8, No.1, [2018].DOI: 10.5121/cseij.2018.8101.
[3]. Sayali Ambekar and Dr.Rashmi Phalnikar. “Disease
Prediction by using Machine Learning”.International journal
of computer engineering and applications, Volume XII,
special issue, May 18. ISSN: 2321-3469.
[4]. Lohith S Y, Dr. Mohamed Rafi. “Prediction of Disease
Using Learning over Big Data -Survey”.International Journal
on Future RevolutioninComputerScience&Communication
Engineering. ISSN: 2454-4248.
[5]. J. Senthil Kumar, S. Appavu. “The Personalized Disease
Prediction Care from Harm using Big Data Analytics in
Healthcare”. Indian Journal of Science and Technology, vol
9(8), DOI: 10.17485/ijst/2016/v9i8/87846, [2016]. ISSN
(Print): 0974-6846, ISSN (Online): 0974-5645.
[6] Gakwaya Nkundimana Joel, S. Manju Priya. “Improved
Ant Colony on Feature Selection and Weighted Ensemble to
Neural Network Based Multimodal Disease Risk Prediction
(WENNMDRP) Classifier for Disease Prediction Over Big
Data”. International Journal of Engineering & Technology,
7(3.27) (2018) 56-61.
[7] Asadi Srinivasulu, S.Amrutha Valli, P.Hussainkhan, and
P.Anitha. “A Survey on Disease Prediction in big data
healthcare using extended convolutional neural network”.
National conference on Emerging Trends in information,
management and Engineering Sciences, [2018].](https://image.slidesharecdn.com/irjet-v6i6512-191209060819/85/IRJET-Survey-on-Risk-Estimation-of-Chronic-Disease-using-Machine-Learning-3-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2429
[8] Stephen J.Mooney and Vikas Pejaver. “Big data in public
health:Terminology,MachineLearning,andPrivacy”,Annual
Review of public Health [2018].
[9] Smriti Mukesh Singh, Dr. Dinesh B. Hanchate.“Improving
Disease Prediction by MachineLearning”. eISSN:2395-0056,
p-ISSN:2395-0072. [10]. Joseph,Nisha,andB.Senthil Kumar.
"Top-K Competitor Trust Mining and Customer Behavior
Investigation Using Data Mining Technique."Journal of
Network Communications and Emerging Technologies
(JNCET) www. jncet. org 8.2 (2018).
[11]. Kumar, B. Senthil. "Adaptive Personalized Clinical
Decision Support System Using Effective Data Mining
Algorithms." Journal of Network Communications and
Emerging Technologies (JNCET) www. jncet. org 8.1(2018).
[12]. Unnikrishnan, Asha, and B. Senthil Kumar. "Biosearch:
A Domain Specific Energy Efficient Query Processing and
Search Optimization in HealthcareSearchEngine." Journal of
Network Communications and Emerging Technologies
(JNCET) www. jncet. org 8.1 (2017).
[13]. Kumar, B. Senthil. "Adaptive Personalized Clinical
Decision Support System Using Effective Data Mining
Algorithms." Journal of Network Communications and
Emerging Technologies (JNCET) www. jncet. org 8.1(2017).
[14]. Kumar, B. Senthil. "Data Mining Methods and
Techniques forClinical DecisionSupportSystems." Journal of
Network Communications and Emerging Technologies
(JNCET) www. jncet. org 7.8 (2017).](https://image.slidesharecdn.com/irjet-v6i6512-191209060819/85/IRJET-Survey-on-Risk-Estimation-of-Chronic-Disease-using-Machine-Learning-4-320.jpg)
IRJET- Survey on Risk Estimation of Chronic Disease using Machine Learning
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2426 Survey on Risk Estimation of Chronic Disease using Machine Learning Vijayalakshmi C.S 1, Dr. Niharika Kumar 2 1Student, Dept. of Computer Science and Engineering, BNMIT, Karnataka, India 2Associate Professor, Dept. of Computer Science and Engineering, BNMIT, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Healthcare is an inevitable task to be done in human life. The health care industry contains lots of medical data, therefore machine learning algorithms are required to make decisions effectively in the prediction of heart diseases. Healthcare sectors generate massive amounts of information about patients. Health care providers can take advantage of machine learning to predict the behavior of the patients. Machine learning provides a way to find the patterns and reason about the data. Recent research has developed into uniting these techniques to provide hybrid machine learning algorithms. Predicting patients’ risk of developing certain diseases is an important research topic in healthcare. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease- frequent communities. We experimentthemodifiedprediction models over real-life hospital data. We propose a new convolutional neural network (CNN) based multimodal disease risk prediction algorithm using structured and unstructured data from hospital. The paper additionally describes the term Unimodal Disease Risk Prediction (UDRP) and it compares and analyze with the performance. Key Words: Big data Analytics, Machine Learning, Healthcare. 1. INTRODUCTION Chronic diseases have been among the major concerns in medical fields since they may cause a heavy burden on healthcare resources and disturb the quality of life. Now a days the healthcare fieldsare makinggreatprogresswiththe rapid development of latest information technologies. In particular, Electronic Medical Records (EMRs)havebrought significant benefits to medical applications. EMRs are sequential with large-scale, which include the records of hospital visits through the whole lifetime of people, like personal profile, diagnostic records, treatment records, etc. Heart is an important organ of all living individual, which plays an essential role of blood pumping to the rest of the organs through the blood vessels ofthecirculatorysystem.If circulation of blood in body is impropertheorganslikebrain suffer and if heart stops working altogether and death occurs. Life is completely dependent on proper working of the heart. Prediction of heart diseases is most complicated and challenging task in the field of the medical science.Heart is one of the most common reason of death in India or other Asian countries. In 2003 approx 17.3 million people died all over globe and out of this,10 million were only due to coronary heart diseases. Along without changing lifestyle there are many such factors such as smoking, alcohol, obesity. High blood pressure, diabetes etc. which are responsible for the risk of having heart problem. The small description of previous system is, it is based and used the data mining concept of machine learning algorithm for effected prediction. This prediction is predicted the disease outbreak. This heart disease outbreak is solved in disease- frequent communities. Heart Disease The heart is one of the body parts that are vital foreverypart of the body by circulating or pumping blood to each body part. If circulation of blood in body is improper the organs like brain suffer and heart stops working altogether and death occurs. So that life ultimately depends on the heart. A properly functioning heart helps the individuals to have a healthy life. Prediction of cardiovascular disease is challenging and more complicated task to achieve an automatic diagnosis of sickness. Because an enormous amount of data are stored in healthcare centersthatarevery complex and challenging to analyses. Even if it ischallenging task using prediction of heart diseases in medical centers is plays significant roles to save the lifestyle of individuals and to make active and accurate decision-making for stakeholders. Some of the most common heart diseases are listed in the table below with their description TABLE - 1: TYPES OF HEART DISEASE There are also different heart disease factors,fromthatmost common are listed in the table below with their symptom.
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2427 TABLE - 2: FACTORS OF HEART DISEASE 2. LITERATURE REVIEW In [1] author has presented the concept namely, “Disease prediction using Machine Learning over Big Data”. The big data is fastest concept in current trend, so this concept is applied in more fields. The big data is most widely used in each every field because it is very large. The big data is applied in medical field both side developing the better growth in both fields, that is big data is applied in medical fields develops the medical fields at the same time increase the growth in big data field. The big data helps to achievethe better growth in medical and health care sectors. It additionally, provides the moremeritsgives,(i)medical data analysis with accuracy, (ii) early prediction for disease, (iii) patient oriented data with accuracy, (iv) The medical data,is securely stored and used in many places, (v) incomplete regional data are reduced and give the accuracy result. Goal of the concept is to choose the region and collects the hospital data or medical data of particular selected region, this process is using the machine learning algorithm. This term based on the data mining technique is used for disease prediction with accuracy. Then, finding the missing data based on latent factor to access the incomplete data and it is reduced. The previous system use the CNNUDRP (Unimodal Disease Risk Prediction), then continuously implements the next level use the CNN-MDRP (Mulimodal Disease Risk Prediction). The CNN-MDRP is overcome the drawback of CNN-UDRP. The CNN-MDRP consists of the hospital data, that is structured and unstructured data. The CNNMDRPalgorithm based prediction is more accurate,thisaccuracyiscompared with previous system. The advantages of the concept is, better feature description and better accuracy, and the disadvantages of this system is, this feature is only applicable for the structured data so it is not good in disease description. Authors, In [2] have proposed in to the concept is machine learning based disease prediction using the big data for overcome the machine learning drawbacks. The smooth progress of big data is moves in the biomedical and healthcare communities in hospital for accurate results in any experiment result. This concept is (a) reduces the incomplete data and (b) effective disease prediction. In [3] author has presentedthedata miningconcept“Disease Prediction by using Machine Learning”.Thedata mining best growth of the stage is develops that technique into the healthcare basis, the data analysis is an important part of every field. The data mining is predicts the information for healthcare is called rapid growth of medical care field. The existing one is designed the purpose of (i) analyze, (ii) manage, (iii) predict of healthcare data, it is described the overall healthcare systems. The concept ofmachinelearning is applied into the disease-relatedinformationretrievalsand the treatment processes in these types of process are achieved by using the data analysis. The predictions of outbreaks in diseases are using the decision tree, because it is very effective. Thisconceptbasedexperimental showsthat result is related to the disease symptoms, so that data is described medical data using modified prediction model. If the concept choose the raining set like medical patient symptoms, than, use thedecisiontree,then,predicted,finally give the symptoms of patient and get the accurate result for disease prediction. This concept is only performs, that is predicts only the patient related information with low time and low cost. Authors, presents In [4] for “prediction of disease using machine learning over big data”. Can develop the medical specialty basis this conceptisappliedtoproduce the medical data in to mass medical data, which means the data which is enlarged. The goal of this concept is targeted the simplest data is stored into the space of medical massive data analysis, called “medical data analysis in massive collection”. It produces the accuracy and it reaches the 4.8% speed faster the CNN-UDRP. It only focuses this three data, (a) structured data, (b) text data, (c) structured and text data. In this proposed system is improves the medical data oriented term. Concept presented by author, In [5] delivered theme is, “personalized disease prediction care from harm using big data”, for healthcare analysis. This concept describes the medical field is a rich data industry because it holds the healthcare records, also. The daily treatment records are increased in every day that is it includes number of transactions, and the patient information is stored and retrieved from the database. The medical treatment records are every day updated one, because every day improves the patient health improvementsbasedontreatment.Itgivesthe correct solutions for different types of diseases. This system is change medical record, which means manually noted every medical oriented record intotheelectronicrecordthat is, digitalize the medical care. This technology is simply
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2428 called, “e-healthcare”. The medical data is stored in the database. The big data methods and the logics are used to analyze the statistical analytics. The proposed system is known as, “disease recommendation system”, and this system holds the specialized tool, this tool is creating the profile. The profile making needs someinformationfromthe personalized persons, that is doctors, patients, etc, If entering the required field of the system finally get the personalized model health profile, but this personalization includes huge number of profiling information and other data. Enthusiastically presents the author, give the information are collected by In [6] namely, “Use the Weighted Ensemble to Neural Network based Multimodal Disease Risk Prediction (WENNMDRP) andfeatureselection of Ant colony improved classifier for disease predictionover the big data concepts”. This concept feature selection performance is gives the dataset, this data set making is one the significant task. The feature selection task is splits into level by level, (i) first, structured the normal and more explainable models, (ii) apply the concept knowledge and learning its performance, (iii) finally, ready to preparing the clean, that is clear the data. Then, the proposed concept is analyzes the feature selection difficulties for big data based data analytics, so resolving this complexity by using the Improved Ant Colony Optimization (IACO) technique. This technique is early solves the missing data problem in incomplete data, which means it before set the latent factor mode, also. But is not easily selects the best feature from the medical data. Table 3 Comparison table 3. CONCLUSION In the survey discuss Disease predicts the hospital data by using the different data mining technique. This analyze the medical data in multiple ways, like that, multidimensional ways and view based collects that data and it escapes the hard risks then, prediction is easily completed. The hospital data is classified in to structureddata andunstructureddata. The concept fulfill the existing system focused both types of data prediction in medical area, that is big data analytics. There are numerous researches from various domains are continuouslyworkingtowardsdevelopingAchievingDisease Prediction. The aim of this survey was to Summarize the recent researches and its demerits towards achieve Disease Prediction. This paper gives the merits and demerits of the recent techniques and its capabilitiesarestudied.Thispaper concludes that there is no effective method discovers for Achieving Disease Prediction. So, further approachesshould overcome all the above issues. Further implementation has to be done in order to Achieving High Disease Prediction using machine learning algorithm. REFERENCES [1]. Shraddha Subhash Shirsath, Prof. Shubhangi Patil Disease Prediction Using Machine Learn.Over Big Data”. I international Journal of Innovative Research in Science, Engineering and Technology, [2018]. ISSN (Online) : 2319- 8753, ISSN (Print) : 2347-6710. [2]. Vinitha S, Sweetlin S, Vinusha H, Sajini S. “Disease Prediction Using Machine Learning Over Big Data”. Computer Science & Engineering: An International Journal (CSEIJ), Vol.8, No.1, [2018].DOI: 10.5121/cseij.2018.8101. [3]. Sayali Ambekar and Dr.Rashmi Phalnikar. “Disease Prediction by using Machine Learning”.International journal of computer engineering and applications, Volume XII, special issue, May 18. ISSN: 2321-3469. [4]. Lohith S Y, Dr. Mohamed Rafi. “Prediction of Disease Using Learning over Big Data -Survey”.International Journal on Future RevolutioninComputerScience&Communication Engineering. ISSN: 2454-4248. [5]. J. Senthil Kumar, S. Appavu. “The Personalized Disease Prediction Care from Harm using Big Data Analytics in Healthcare”. Indian Journal of Science and Technology, vol 9(8), DOI: 10.17485/ijst/2016/v9i8/87846, [2016]. ISSN (Print): 0974-6846, ISSN (Online): 0974-5645. [6] Gakwaya Nkundimana Joel, S. Manju Priya. “Improved Ant Colony on Feature Selection and Weighted Ensemble to Neural Network Based Multimodal Disease Risk Prediction (WENNMDRP) Classifier for Disease Prediction Over Big Data”. International Journal of Engineering & Technology, 7(3.27) (2018) 56-61. [7] Asadi Srinivasulu, S.Amrutha Valli, P.Hussainkhan, and P.Anitha. “A Survey on Disease Prediction in big data healthcare using extended convolutional neural network”. National conference on Emerging Trends in information, management and Engineering Sciences, [2018].
- 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2429 [8] Stephen J.Mooney and Vikas Pejaver. “Big data in public health:Terminology,MachineLearning,andPrivacy”,Annual Review of public Health [2018]. [9] Smriti Mukesh Singh, Dr. Dinesh B. Hanchate.“Improving Disease Prediction by MachineLearning”. eISSN:2395-0056, p-ISSN:2395-0072. [10]. Joseph,Nisha,andB.Senthil Kumar. "Top-K Competitor Trust Mining and Customer Behavior Investigation Using Data Mining Technique."Journal of Network Communications and Emerging Technologies (JNCET) www. jncet. org 8.2 (2018). [11]. Kumar, B. Senthil. "Adaptive Personalized Clinical Decision Support System Using Effective Data Mining Algorithms." Journal of Network Communications and Emerging Technologies (JNCET) www. jncet. org 8.1(2018). [12]. Unnikrishnan, Asha, and B. Senthil Kumar. "Biosearch: A Domain Specific Energy Efficient Query Processing and Search Optimization in HealthcareSearchEngine." Journal of Network Communications and Emerging Technologies (JNCET) www. jncet. org 8.1 (2017). [13]. Kumar, B. Senthil. "Adaptive Personalized Clinical Decision Support System Using Effective Data Mining Algorithms." Journal of Network Communications and Emerging Technologies (JNCET) www. jncet. org 8.1(2017). [14]. Kumar, B. Senthil. "Data Mining Methods and Techniques forClinical DecisionSupportSystems." Journal of Network Communications and Emerging Technologies (JNCET) www. jncet. org 7.8 (2017).