IRJET- Prediction and Analysis of Heart Disease using SVM Algorithm
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This document describes a study that uses a support vector machine (SVM) algorithm to predict heart disease based on patient data. The study uses a dataset of 1000 patient records with 8 attributes related to risk factors for heart disease. The SVM algorithm is applied to identify patterns in the data and classify patients as having heart disease or not. It aims to find the optimal decision boundary between the two classes to minimize classification errors. The results show that the SVM technique can accurately predict heart disease based on the risk factor attributes in the patient data.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 872
Prediction and Analysis of Heart Disease using SVM Algorithm
Madhura Patil1, Rima Jadhav2, vishakha Patil3, Aditi Bhawar4, Mrs. Geeta Chillarge5
1,2,3,4Students, Dept. of Computer Engineering, Marathwada Mitra Mandal’s College Of Engineering, Pune,
Maharashtra, India
5Professor, Dept. of Computer Engineering, Marathwada Mitra Mandal’s College Of Engineering, Pune,
Maharashtra, India
-------------------------------------------------------------------------***------------------------------------------------------------------------
Abstract – Heart disease prediction usingdata miningisone
of the most interesting and challenging tasks. The shortage
of specialists and high wrongly diagnosed cases has
necessitated the need to develop a fast and efficient
detection system. According topastsystemtheintegrationof
clinical decision supportwithcomputerbasedpatientrecord
can reduce medical errors, can be made more precise and
hence enhance patient safety. We are providing a system
which can help for prediction ofheartdisease byconsidering
risky factor associated with heart disease. Here system
applies support vector machine algorithm on historical
information/data of patient and itprovidesfeatureslikeAge,
Sex, Smoking, Overweight, Alcohol Intake, Bad Cholesterol,
Blood Pressure and Heart Rate to make prediction of
coronary heart disease with higher accuracy is done.
Keywords: Heart disease, Data mining, Support Vector
Machine, Risky Factor.
1. INTRODUCTION
Life is completely dependent on efficient working of the
heart. The term Heart disease refers to disease of heart
blood vessel system within tithe heart is an importantorgan
of human body. If the blood circulation to the body is
inadequate, the organs of the body that is brain and heart
stop working and death occurs in fewminutes.Heartdisease
is a leading cause of death worldwide from past 15 years.
The common risk factors associated are identified as age,
family history, Sex, Stress, high cholesterol, Heart rate,
smoking, alcohol intake, overweight, physical inactivity,
chest pain type and poor diet. Information obtained by
examining the history record of the patient, it is possible to
isolate the record and give report on HD if it is positive or
negative.
Heart disease is the most common cause of death globally.
Many hospital information systems are designed to support
patient billing, inventory management and generation of
simple statistics. Some hospitals use decision support
systems, but they are largely limited. Mining is a method of
exploring massive sets of data to take out patternswhich are
hidden and previously unknown relationships and
knowledge detection to help the better understanding of
medical data to prevent heart disease. Classification of
coronary Heart Disease can be valuable for the medical
practitioners in the event that it is automated with the end
goal of quick finding and exact result. Presence of heart
disease precisely can spare patients living days. The work
incorporates the classes of Heart Disease utilizing Support
Vector Machine (SVM). In this a medical choice backing
framework for coronary illness characterization in a sane,
purpose, precise and fast manner.
In this system first we analyze the historydata ofpatientand
by getting risky factor disease is predicted using support
vector machine.
2. LITERATURE REVIEW
The survey is carried out on different techniques used in
detection of HD. Different technologies and rich survey is
available for heart disease prediction model.
There are many classification techniques involving Naive
bayes (NB), Decision tree (DT), Neural network (NN),
Genetic algorithm (GA), Artificial intelligence (AI) and
Clustering algorithms like KNN, and Supportvectormachine
(SVM). The paper gives [3]predictionmodel usingindividual
technique and also by combining two or more techniques.
This paper provides a quick and easy review and
understanding of available prediction models using data
mining from 2004 to 2016.
Miss. Chaitrali S et al.[1]developed Heart Disease Prediction
system (HDPS) using Neural network. The HDPS system
predicts the likelihood of patient getting a Heartdisease.For
prediction, the system uses sex, blood pressure, cholesterol
like 13 medical parameters. Here two more parameters are
added i.e. obesity and smoking for better accuracy.
’D. Mendes et al. [2 ] gives a simple and interpretable model
based on a real dataset. It consists of a decision tree model
structure that uses a reduced set of six binary risk factors.
The justification is performedusinga recentdatasetgivenby
the Portuguese Society of Cardiology which originally
comprised 77 risk factors.
This[4] paper gives a frequent feature selection method for
Heart Disease Prediction. Use of the fuzzy measure and the
relevant nonlinear integral gives good performance. The
none additively of the fuzzy measure reflectstheimportance
of the feature attributes as well as their interactions. Using
features such as age, sex, blood pressure and blood sugar it
can predict the likelihood of patients getting a heart disease.](https://image.slidesharecdn.com/irjet-v6i5177-190824053509/85/IRJET-Prediction-and-Analysis-of-Heart-Disease-using-SVM-Algorithm-1-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 873
And this improves the accuracy and reduces the
computational time.
Fizar Ahmed et al.[5] gives the architecture for heart rate
and other data monitoring technique and also how to use a
machine learningtechniquelikekNN classificationalgorithm
to forecast the heart attack by with the set of heart rate data
and other parameter associated with heart.
3. PROPOSED FRAMEWORK
3.1 INTRODUCTION
Cardiovascular disease is the leading global causeofdeath.A
normal heart rate is 60-100 beats per minute. However,
heart rate higher than 76 beats per minute when in resting
may be linked to a higher risk of heart attack. Having an
irregular heartbeat doesn’t meanhavinga heartattack.Butif
it’s a new symptom, or if you have chest pains or problems
breathing, may be the preliminary symptomforheartattack.
Figure shows the architecture diagram of the proposed
system by using which we can predict thediseasedepending
on the parameters mentioned.
3.2 PROJECT SCOPE
According to past system the integration of clinical decision
support with computer based patient record can reduce
medical errors, can be made more precise and hence
enhance patient safety. We are providinga systemwhichcan
help for prediction of heart disease by considering risky
factor associated with heart disease. Here we getting
historical information/data of patient. By appling support
vector machine algorithm on featureslikeAge,Sex,Smoking,
Overweight, Alcohol Intake, Bad Cholesterol,Blood Pressure
and Heart Rate to make prediction of coronary heartdisease
with higher accuracy is done.
3.3 DATASET OF PATIENT
The Directory or the website name
http://archive.ics.uci.edu/ml/datasets/heart+Disease
contains 4 databases concerningheartdiseasediagnosis. All
attributes are numeric-valued. The data was collected from
the four following locations:
1. ClevelandClinic Foundation (cleveland.data)
2. Hungarian Institute of Cardiology, Budapest
(hungarian.data)
3. V.A. Medical Center, Long Beach, CA(long-beach-va.data)
4. University Hospital, Zurich, Switzerland
(switzerland.data). Each database has the same instance
format. While the databases have 76 raw attributes, only
14 of them are actually used. Thus I've taken the liberty of
making 2 copies of each database: one with all the attributes
and 1 with the 14 attributes actually used in past
experiments.
3.4 PROCESSING OF DATA
Identify or study on heart disease patients data and then
decides which factors are common in same heart disease
patient. Experiments were conducted with Weka. 3.6.0 tool.
Data set of 1000 records with 8 attributes is used. The
results of our experimental analysis in finding significant
patterns for heart attack prediction are presented in this
section.
3.5 SVM ALGORITHM
Support vector machine (SVM) proposed by Vapnik and
Cortes have been successfully applied for gender
classification problems by many researchers. An SVM
classifier is a linear classifier where the separating hyper
plane is chosen to minimize the expected classification error
of the unseen test patterns.
SVM is a strong classifier which can identify two classes.
SVM classifies the test image to the class which has the
maximum distance to the closest point in the training.
SVM training algorithm built a model that predict whether
the test image fall into this class or another. SVM require a
huge amount of training data to select an affective decision
boundary and computational cost is very high even if we
restrict ourselves to single pose (frontal) detection.
The SVM is a learning algorithm for classification. It tries to
find the optimal separating hyper plane such that the
expected classification error for unseen patterns is
minimized. For linearly non-separable data the input is
mapped to high-dimensional feature space where they can
be separated by a hyper plane. This projection into high-
dimensional feature space is efficiently performed by using
kernels. More precisely, given a set of training samples and
the corresponding decision values -1, 1 the SVM aims to find
the best separating hyper plane given by the equation
WTx+b that maximizesthedistancebetweenthetwoclasses.
3.6 RESULT
Analyze the sensors data on server by applying SVM data
mining technique which is useful in our scenario. From
these, conclusions to the most effective model, the efficacy
of conjoint models, and the final accuracy of the overall
model can be drawn.](https://image.slidesharecdn.com/irjet-v6i5177-190824053509/85/IRJET-Prediction-and-Analysis-of-Heart-Disease-using-SVM-Algorithm-2-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 874
4. DATAFLOW DIAGRAM
Figure 1:Data Flow diagram
5. CONCLUSION
Heart Disease is a fatal disease by its nature. This disease
makes a life threatening complexities such as heart attack
and death. The importance of Data Mining in the Medical
Domain is realized and steps are taken to apply relevant
techniques in the Disease Prediction. Weareimplementinga
system which will helptopredictheartdiseasedepending on
the patients clinical data relatedtothefactorassociated with
heart disease. By using medical dataset of the patients such
as age, sex, blood pressure, overweight and blood sugar and
by appling SVM classifier we can predict that the patients
getting a heart disease or not. In addition classification
accuracy, sensitivity, and specificity of the SVM have been
found to be high thus making it a superior alternativeforthe
diagnosis. We are also doing analysis on thedata fromwhich
we are getting at which age it mostly occur or which region
gets influenced by that disease.Soprecautioncanbetakento
avoid the death due to the heart disease..
REFERENCES
[1] Miss. Chaitrali S. Dangare, Dr. Mrs. Sulabha S.
Apte,“A DATA MINING APPROACH FOR
PREDICTION OF HEART DISEASE USING NEURAL
NETWORKS”,INTERNATIONAL JOURNAL OF
COMPUTERENGINEERINGandTECHNOLOGY,2012.
[2] D. Mendes, S. Paredes, T. Rocha, P. Carvalho
“Assessment of Cardiovascular Risk based on a
Data-driven Knowledge Discovery Approach”,2015
IEEE.
[3] Salma Banu N.K,Suma Swamy“Prediction of Heart
Disease at early stage using Data Mining and Big
Data Analytics: A Survey”,2016 International
Conference on Electrical, Electronics,
Communication, Computer and Optimization
Techniques (ICEECCOT),2016 IEEE.
[4] SunitaSoni, JyotiSoni,UjmaAnsari,Dipesh Sharma,
“Predictive Data Mining for Medical Diagnosis: An
Overview of Heart Disease Prediction”,
International J ournal of Computer Application
(IJCA, 0975 8887) Volume 17 No.8, March 2011.
[5] Fizar Ahmed,”An Internet of Things (IoT)
Application for Predicting the Quantity of Future
Heart Attack Patients “ International Journal of
ComputerApplications(09758887)Volume164No
6, April 2017.
[6] Moeen Hassanalieragh,Alex Page, Tolga Soyata,
Gaurav Sharma, Mehmet Aktas, Gonzalo Mateos,
Burak Kantarci, Silvana Andreescu, Health
Monitoring and Management Using Internet-of-
Things (IoT) Sensing with Cloud-based Processing:
Opportunities and Challenges 2015 IEEE
International Conference on Services Computing.
[7] Ximeng Liu, Member, IEEE, Robert H. Deng, Fellow,
IEEE, Kim-Kwang Raymond Choo, Senior Member,
IEEE, Yang Yang, Member, IEEE,PrivacyPreserving
Outsourced Clinical Decision Support System in the
Cloud, IEEE TRANSACTION ON SERVICES
COMPUTING. 2017.
[8] Poornima Singh,1 Sanjay Singh,2 and Gayatri S
Pandi-Jain1"Effective heart disease prediction
system using data mining techniques",Published
online 2018 Mar 15. doi: . [10.2147/IJN.S124998].
[9] Vikas Chaurasia,"Early Prediction of HeartDiseases
Using Data Mining Techniques",December 2013
[10] R Ani ; S Krishna ; N Anju ; M Sona Aslam ; O. S.
Deepa "Iot based patient monitoring and diagnostic
prediction tool using ensemble classifier",
[11] Hai Wang et. al.,”Medical Knowledge Acquisition
through Data Mining”, Proceedings of 2008 IEEE
International Symposium on IT in Medicine and
Education 978 - 1 - 4244 - 2511 - 2/08©2008
Crown.
[12] Sugondo Hadiyoso, 2Koredianto Usman, 3Achmad
Rizal"Arrhythmia Detection Based on ECG Signal
Using Android Mobile for Athlete andPatient",2015
3rd International Conference on Information and
Communication Technology (ICoICT).
[13] Gorav Kumar Malik,Dr.Yatindra Kumar,Dr. Manoj
Panda, "Cardiah arrhythmia detehtion in ECG
signals by feature Extrahtion and support vehtor
mahhine",Proceedings of the Second International
Conference on Research in Intelligent and
Computing in Engineering pp. 241–244 DOI:
10.15439/2017R63 ACSIS, Vol. 10 ISSN 2300-596.](https://image.slidesharecdn.com/irjet-v6i5177-190824053509/85/IRJET-Prediction-and-Analysis-of-Heart-Disease-using-SVM-Algorithm-3-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 875
[14] J. P. Kelwade,S. S. Salankar"Prediction of C ardiac
Arrhythmia using Artificial Neural N etwork",
International Journal of Computer Applications
(0975 – 8887) Volume 115 – No. 20 , April 2015.
[15] Alka S. Barhatte ; Rajesh Ghongade ; Abhishek S.
Thakare "RS complex detection and arrhythmia
classification using SVM", 2015 Communication,
Control and Intelligent Systems (CCIS).](https://image.slidesharecdn.com/irjet-v6i5177-190824053509/85/IRJET-Prediction-and-Analysis-of-Heart-Disease-using-SVM-Algorithm-4-320.jpg)
IRJET- Prediction and Analysis of Heart Disease using SVM Algorithm
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 872 Prediction and Analysis of Heart Disease using SVM Algorithm Madhura Patil1, Rima Jadhav2, vishakha Patil3, Aditi Bhawar4, Mrs. Geeta Chillarge5 1,2,3,4Students, Dept. of Computer Engineering, Marathwada Mitra Mandal’s College Of Engineering, Pune, Maharashtra, India 5Professor, Dept. of Computer Engineering, Marathwada Mitra Mandal’s College Of Engineering, Pune, Maharashtra, India -------------------------------------------------------------------------***------------------------------------------------------------------------ Abstract – Heart disease prediction usingdata miningisone of the most interesting and challenging tasks. The shortage of specialists and high wrongly diagnosed cases has necessitated the need to develop a fast and efficient detection system. According topastsystemtheintegrationof clinical decision supportwithcomputerbasedpatientrecord can reduce medical errors, can be made more precise and hence enhance patient safety. We are providing a system which can help for prediction ofheartdisease byconsidering risky factor associated with heart disease. Here system applies support vector machine algorithm on historical information/data of patient and itprovidesfeatureslikeAge, Sex, Smoking, Overweight, Alcohol Intake, Bad Cholesterol, Blood Pressure and Heart Rate to make prediction of coronary heart disease with higher accuracy is done. Keywords: Heart disease, Data mining, Support Vector Machine, Risky Factor. 1. INTRODUCTION Life is completely dependent on efficient working of the heart. The term Heart disease refers to disease of heart blood vessel system within tithe heart is an importantorgan of human body. If the blood circulation to the body is inadequate, the organs of the body that is brain and heart stop working and death occurs in fewminutes.Heartdisease is a leading cause of death worldwide from past 15 years. The common risk factors associated are identified as age, family history, Sex, Stress, high cholesterol, Heart rate, smoking, alcohol intake, overweight, physical inactivity, chest pain type and poor diet. Information obtained by examining the history record of the patient, it is possible to isolate the record and give report on HD if it is positive or negative. Heart disease is the most common cause of death globally. Many hospital information systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Mining is a method of exploring massive sets of data to take out patternswhich are hidden and previously unknown relationships and knowledge detection to help the better understanding of medical data to prevent heart disease. Classification of coronary Heart Disease can be valuable for the medical practitioners in the event that it is automated with the end goal of quick finding and exact result. Presence of heart disease precisely can spare patients living days. The work incorporates the classes of Heart Disease utilizing Support Vector Machine (SVM). In this a medical choice backing framework for coronary illness characterization in a sane, purpose, precise and fast manner. In this system first we analyze the historydata ofpatientand by getting risky factor disease is predicted using support vector machine. 2. LITERATURE REVIEW The survey is carried out on different techniques used in detection of HD. Different technologies and rich survey is available for heart disease prediction model. There are many classification techniques involving Naive bayes (NB), Decision tree (DT), Neural network (NN), Genetic algorithm (GA), Artificial intelligence (AI) and Clustering algorithms like KNN, and Supportvectormachine (SVM). The paper gives [3]predictionmodel usingindividual technique and also by combining two or more techniques. This paper provides a quick and easy review and understanding of available prediction models using data mining from 2004 to 2016. Miss. Chaitrali S et al.[1]developed Heart Disease Prediction system (HDPS) using Neural network. The HDPS system predicts the likelihood of patient getting a Heartdisease.For prediction, the system uses sex, blood pressure, cholesterol like 13 medical parameters. Here two more parameters are added i.e. obesity and smoking for better accuracy. ’D. Mendes et al. [2 ] gives a simple and interpretable model based on a real dataset. It consists of a decision tree model structure that uses a reduced set of six binary risk factors. The justification is performedusinga recentdatasetgivenby the Portuguese Society of Cardiology which originally comprised 77 risk factors. This[4] paper gives a frequent feature selection method for Heart Disease Prediction. Use of the fuzzy measure and the relevant nonlinear integral gives good performance. The none additively of the fuzzy measure reflectstheimportance of the feature attributes as well as their interactions. Using features such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease.
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 873 And this improves the accuracy and reduces the computational time. Fizar Ahmed et al.[5] gives the architecture for heart rate and other data monitoring technique and also how to use a machine learningtechniquelikekNN classificationalgorithm to forecast the heart attack by with the set of heart rate data and other parameter associated with heart. 3. PROPOSED FRAMEWORK 3.1 INTRODUCTION Cardiovascular disease is the leading global causeofdeath.A normal heart rate is 60-100 beats per minute. However, heart rate higher than 76 beats per minute when in resting may be linked to a higher risk of heart attack. Having an irregular heartbeat doesn’t meanhavinga heartattack.Butif it’s a new symptom, or if you have chest pains or problems breathing, may be the preliminary symptomforheartattack. Figure shows the architecture diagram of the proposed system by using which we can predict thediseasedepending on the parameters mentioned. 3.2 PROJECT SCOPE According to past system the integration of clinical decision support with computer based patient record can reduce medical errors, can be made more precise and hence enhance patient safety. We are providinga systemwhichcan help for prediction of heart disease by considering risky factor associated with heart disease. Here we getting historical information/data of patient. By appling support vector machine algorithm on featureslikeAge,Sex,Smoking, Overweight, Alcohol Intake, Bad Cholesterol,Blood Pressure and Heart Rate to make prediction of coronary heartdisease with higher accuracy is done. 3.3 DATASET OF PATIENT The Directory or the website name http://archive.ics.uci.edu/ml/datasets/heart+Disease contains 4 databases concerningheartdiseasediagnosis. All attributes are numeric-valued. The data was collected from the four following locations: 1. ClevelandClinic Foundation (cleveland.data) 2. Hungarian Institute of Cardiology, Budapest (hungarian.data) 3. V.A. Medical Center, Long Beach, CA(long-beach-va.data) 4. University Hospital, Zurich, Switzerland (switzerland.data). Each database has the same instance format. While the databases have 76 raw attributes, only 14 of them are actually used. Thus I've taken the liberty of making 2 copies of each database: one with all the attributes and 1 with the 14 attributes actually used in past experiments. 3.4 PROCESSING OF DATA Identify or study on heart disease patients data and then decides which factors are common in same heart disease patient. Experiments were conducted with Weka. 3.6.0 tool. Data set of 1000 records with 8 attributes is used. The results of our experimental analysis in finding significant patterns for heart attack prediction are presented in this section. 3.5 SVM ALGORITHM Support vector machine (SVM) proposed by Vapnik and Cortes have been successfully applied for gender classification problems by many researchers. An SVM classifier is a linear classifier where the separating hyper plane is chosen to minimize the expected classification error of the unseen test patterns. SVM is a strong classifier which can identify two classes. SVM classifies the test image to the class which has the maximum distance to the closest point in the training. SVM training algorithm built a model that predict whether the test image fall into this class or another. SVM require a huge amount of training data to select an affective decision boundary and computational cost is very high even if we restrict ourselves to single pose (frontal) detection. The SVM is a learning algorithm for classification. It tries to find the optimal separating hyper plane such that the expected classification error for unseen patterns is minimized. For linearly non-separable data the input is mapped to high-dimensional feature space where they can be separated by a hyper plane. This projection into high- dimensional feature space is efficiently performed by using kernels. More precisely, given a set of training samples and the corresponding decision values -1, 1 the SVM aims to find the best separating hyper plane given by the equation WTx+b that maximizesthedistancebetweenthetwoclasses. 3.6 RESULT Analyze the sensors data on server by applying SVM data mining technique which is useful in our scenario. From these, conclusions to the most effective model, the efficacy of conjoint models, and the final accuracy of the overall model can be drawn.
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 874 4. DATAFLOW DIAGRAM Figure 1:Data Flow diagram 5. CONCLUSION Heart Disease is a fatal disease by its nature. This disease makes a life threatening complexities such as heart attack and death. The importance of Data Mining in the Medical Domain is realized and steps are taken to apply relevant techniques in the Disease Prediction. Weareimplementinga system which will helptopredictheartdiseasedepending on the patients clinical data relatedtothefactorassociated with heart disease. By using medical dataset of the patients such as age, sex, blood pressure, overweight and blood sugar and by appling SVM classifier we can predict that the patients getting a heart disease or not. In addition classification accuracy, sensitivity, and specificity of the SVM have been found to be high thus making it a superior alternativeforthe diagnosis. We are also doing analysis on thedata fromwhich we are getting at which age it mostly occur or which region gets influenced by that disease.Soprecautioncanbetakento avoid the death due to the heart disease.. REFERENCES [1] Miss. Chaitrali S. Dangare, Dr. Mrs. Sulabha S. Apte,“A DATA MINING APPROACH FOR PREDICTION OF HEART DISEASE USING NEURAL NETWORKS”,INTERNATIONAL JOURNAL OF COMPUTERENGINEERINGandTECHNOLOGY,2012. [2] D. Mendes, S. Paredes, T. Rocha, P. Carvalho “Assessment of Cardiovascular Risk based on a Data-driven Knowledge Discovery Approach”,2015 IEEE. [3] Salma Banu N.K,Suma Swamy“Prediction of Heart Disease at early stage using Data Mining and Big Data Analytics: A Survey”,2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT),2016 IEEE. [4] SunitaSoni, JyotiSoni,UjmaAnsari,Dipesh Sharma, “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction”, International J ournal of Computer Application (IJCA, 0975 8887) Volume 17 No.8, March 2011. [5] Fizar Ahmed,”An Internet of Things (IoT) Application for Predicting the Quantity of Future Heart Attack Patients “ International Journal of ComputerApplications(09758887)Volume164No 6, April 2017. [6] Moeen Hassanalieragh,Alex Page, Tolga Soyata, Gaurav Sharma, Mehmet Aktas, Gonzalo Mateos, Burak Kantarci, Silvana Andreescu, Health Monitoring and Management Using Internet-of- Things (IoT) Sensing with Cloud-based Processing: Opportunities and Challenges 2015 IEEE International Conference on Services Computing. [7] Ximeng Liu, Member, IEEE, Robert H. Deng, Fellow, IEEE, Kim-Kwang Raymond Choo, Senior Member, IEEE, Yang Yang, Member, IEEE,PrivacyPreserving Outsourced Clinical Decision Support System in the Cloud, IEEE TRANSACTION ON SERVICES COMPUTING. 2017. [8] Poornima Singh,1 Sanjay Singh,2 and Gayatri S Pandi-Jain1"Effective heart disease prediction system using data mining techniques",Published online 2018 Mar 15. doi: . [10.2147/IJN.S124998]. [9] Vikas Chaurasia,"Early Prediction of HeartDiseases Using Data Mining Techniques",December 2013 [10] R Ani ; S Krishna ; N Anju ; M Sona Aslam ; O. S. Deepa "Iot based patient monitoring and diagnostic prediction tool using ensemble classifier", [11] Hai Wang et. al.,”Medical Knowledge Acquisition through Data Mining”, Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education 978 - 1 - 4244 - 2511 - 2/08©2008 Crown. [12] Sugondo Hadiyoso, 2Koredianto Usman, 3Achmad Rizal"Arrhythmia Detection Based on ECG Signal Using Android Mobile for Athlete andPatient",2015 3rd International Conference on Information and Communication Technology (ICoICT). [13] Gorav Kumar Malik,Dr.Yatindra Kumar,Dr. Manoj Panda, "Cardiah arrhythmia detehtion in ECG signals by feature Extrahtion and support vehtor mahhine",Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering pp. 241–244 DOI: 10.15439/2017R63 ACSIS, Vol. 10 ISSN 2300-596.
- 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 875 [14] J. P. Kelwade,S. S. Salankar"Prediction of C ardiac Arrhythmia using Artificial Neural N etwork", International Journal of Computer Applications (0975 – 8887) Volume 115 – No. 20 , April 2015. [15] Alka S. Barhatte ; Rajesh Ghongade ; Abhishek S. Thakare "RS complex detection and arrhythmia classification using SVM", 2015 Communication, Control and Intelligent Systems (CCIS).