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REFERNCE

 

In this paper we survey the various techniques used in
data mining and bigdata for prediction of heart disease. Each technique’s
accuracy is known. Bigdata is not much used for processing or analysis. We currently propose to use logistics classifier and K-Means
for prediction of heart disease. In future using big data many enhancements can
be done in the prediction of heart disease process.

CONCLUSION

M SATISH ET AL. (2015) implementing Data mining
techniques like Rule based Decision Tree, Naive bayes etc. They also used
specialized technique pruning classification rules were used to extract
association rules from heart disease warehouses to predict the disease.

LOKANATH SARANGI ET AL. (2015) they are using cost
effective model with the help of genetic algorithm therefore optimized their
weight and fed into the neural network and their corresponding accuracy is
obtained. The accuracy for the particular hybrid technique is 90%.

SHAMSHER BAHADUR PATEL ET AL. (2013) in this system
the 14 attributes is reduced to 6 from 14 then Naive Bayes classifier with the
help of clustering and decision tree is used to predict the heart disease then
genetic algorithm was applied with the following attributes it was implemented
in WEKA tool. The accuracy for Decision tree Naive Bayes, Classclust is 99.2%,
96.5% and 88.3% respectively.

I.S JENZI ET.AL. (2013) used classifier model in data
mining. Association rules along with classification techniques were
implemented. The interrelationship between patterns was brought out. The GUI
used for this purpose is Microsoft .Net platform, with interconnection
performed by IKVM interface with java libraries. The result obtained is from
receiver operating characteristics (ROC) curves and accuracy is obtained.

SYED UMAR ET AL.(2013)combing two data mining
techniques hybrid method was used in which GA optimization benefits are
initiated to improve the Neural Network weight. For learning and                                                                                                                                                                                                                                              training
purpose they implemented back propagation techniques. MLP network was used “12
input 10 hidden 2 output nodes”. The particular back propagation used is “The
Levenberg-Marquardt back propagation algorithm” where bias and weight was
recorded and updated using MATLAB R2012a, Global optimization toolbox and
neural network toolbox application for this purpose. The specified datamining
algorithms used are SVM, Decision tree, multilayer perceptron with an accuracy
rate of 82.5%, 82.5%, 89.7%.

MAI SHOUMAN ET AL. (2012) They proposed a single data
mining technique instead of multiple and also used hybrid technique. The
specified techniques used are kernel density, automatically defined groups,
bagging algorithm and support vector machine was performed. The accuracy found
was 84.1%.

CHAITRALI S.ET AL. (2012) they used 13 attributes
along with two additional attributes for prediction of heart disease. The data
mining methods used are Neural Network, Decision tree, and Naive Bayes.
Accuracy for each was found to be 100%, 99.62% and 90.74% respectively which
was improved from the techniques confusion matrix is calculated since the
accuracy of Neural Network is 100% so it was found to be the most efficient
one.

PETER, T. J. ET AL. (2012) implemented classification
based data mining. The input data set consist of intrinsic linear combination
this cannot be applied for modelling therefore different classification model
was applied to overcome these limitations. Initially the data is cleaned using
data mining techniques, Naive Bayes, KNN, Decision tree and neural network were
implemented on these data collected their accuracy is noted 83.70%, 76.66% and
75.18% Naive Bayes was found to be efficient among all of these .C

K.SIRINIVAS ET AL. (2010) this system is based on
“behaviour risk factor surveillance system” the survey was performed in coal
mining areas like Singareni collieries company in Andhra Pradesh, India. In
these areas it was found that the risk rate of CVD was high compared to other
regions. Patient’s records were collected and diagnosis was done along with the
rest risk factors associated with heart disease was considered ,these records
also provided the morbidity rate of that particular area. The evaluation of the
system was done based on two key measures such as accuracy and sensitivity.

MINAS A.KARAOLIS ET AL. (2009) used event related risk
factor for this process there are two types of event modifiable and
non-modifiable. Myocardial infection, percutaneous coronary intervention was
also a type of event. Then accuracy was obtained 66%, 70% and 75% for each of
the specified event.

SELLAPPAN PALANIAPPAN ET AL. (2008) in this they
developed an artificial intelligence system based on which prediction was
performed they introduced multiple data mining techniques like decision tree,
naive Bayes, neural network etc., which gave the advantage of all these
techniques put together in one. New patterns were discovered, risk patterns
were taken into account there were about 15 attributes therefore the prediction
system was developed. The accuracy of Neural network, Naive Bayes, Decision
tree are 85.68%, 86.12%, 80.4% respectively.

HEON GYU ET AL. (2007) they implemented
multiparametric features like Linear and Nonlinear with high- rate variability
of three postures namely supine, left lateral and right lateral position to
find HRV indices for finding coronary heart disease. Multiple classification
methods were applied like Bayesian classification, SVM and classification based
on multiple classification rules and the accuracy was found to be 81%, 85%,80%
respectively. Statistical analysis was performed and there performance was
noted.

LATHA ET AL. (2007) they used Coactive Neuro Fuzzy
Interface which combined two data mining algorithm (i.e.) Genetic Algorithm and
Neural Network. Hybrid models were also used for prediction process with help
of risk factors associated. Therefore predict the heart disease in the patient.

YANWEI X ET AL. (2007) in this data mining algorithm
were employed to predicting the survival of coronary heart disease this was
done based on 1000 cases. This was done on basis of the observation made on
patient for the past 6 months .There were three data mining techniques employed
and 10 fold cross validation was performed ,they are accurate sensitive and
specificity. Then confusion matrix was calculated, then accuracy was calculated
(i.e.) 92.1%, 91%, 89.6% was obtained from support vector machine, ANN and DT.

HONGMEI YAN ET AL. (2006) use multilayer perception
which has 40 inputs and 5 output layers. Back propagation helps train the 352
medical data collected with the help of assessment models like holdout, cross
validation and bootstrapping. Multilayer perception is system that has good
architecture for neural network. MLP consist of three layers such as input,
hidden and output using which these heart diseases were predicted with an
accuracy of 90%.

PETER LEIJDEKKERS ET.AL (2006) used heart disease
monitoring application embedded in the smart phones and wireless sensor. The
process of analysis is done using this application which can monitor the
patient’s condition to doctor and also provide alarm to the ambulance in case
of emergency. The alert message is sent to the nearby health care centre
therefore the patient’s life can be saved. The model was designed using two
methods Microsoft’s Window Mobile Pocket PC Platform and .Net Compact Framework
extended with OpenNETCF.

CARLOS ORDONEZ (2006) in this the limitations in
association rule was solved, specified algorithm was designed to search
constraint attributes which decreased the set of rules. Bioinformatics
significance was based on support and confidence. In this way prediction was
performed.

CARLOS ORDONEZ (2004) used association rule mining for
prediction of heart disease. It actually was about detection of the disease
with the help of the risk factor and also measured heart perfuser along with
artery depletion was found 2.

LITERATURE SURVEY: Many
papers are reviewed in which different techniques are used for heart disease
prediction.

Since these medical data comprises of huge data so
these can be analysed and processed using data mining techniques. There are
also several tools available for this process. Mining can also be done for this
data, modelling and design can be done. Prediction model for heart disease is
done using data mining in an efficient way and the required outcome is
obtained.

HOW DATA MINING IS USEFUL FOR PREDICTING HEART DISEASE

The heart and blood vessel disease which occurs is
called as cardiovascular disease. The various diseases, disorders and specific
conditions in heart is known as Heart Disease. When considering whole death
rate it is found that the major cause is heart disease .Predicting these
disease at an early stage is essential. The risk factors associated with heart
are Age, family history, obesity, high blood pressure, blood sugar level,
cholesterol, poor diet, smoking, intake of alcohol etc., these are to be
considered in predicting process.

CARDIOVASCULAR DISEASE (CVD) OR HEART DISEASE

Using Data Mining techniques we can easily cluster and
classify data that is collected. They call Data mining as “handler” and Bigdata
as an “asset”, by combining these two analyses, modelling and predicting heart
disease is performed. Using both data mining and big data we can perform
predicting using various techniques and bring out expected outcome

Data mining and Bigdata are used for the analysis of large
amount of data. In fields of medical industry where tons of data available is
processed using data mining techniques. Early times they us MS excel spread
sheet was used to handle huge data where all other techniques were expensive.
Database also used for handling huge data it is very helpful collect the exact
chunk of data required which is relevant and correct. The clients using this
database manager need to prepare queries for information from database through
automation.

DATA MINING AND BIG DATA ANALTICS

INTRODUCTION: Medical   records comprises of vast amount of data
which can be mined. There are unidentified patterns in these data. Heart disease
is an important disease affecting people all around the world which leads to
death since heart is an important organ. The risk factor associated with heart
disease are blood pressure, age, tobacco, smoking, alcohol intake, obesity,
physical inactivity, family history, poor diet, high cholesterol. These
information obtained from doctor’s examination helps to segregate the record
and produce the result. The diagnosis is performed on of Electrocardiogram
(ECG), Echocardiography (ECHO), the previous test result and doctor’s
experience. Data mining extraction methods helps finding knowledge and hidden
patterns from the available data. Data mining is a process of observing these
patterns from the data relevant to the disease so prediction can be performed
using these algorithms and techniques.

ABSTRACT: This paper
helps us to understand the various data mining technologies used in diagnosing
heart disease in patients. Datasets of patient details are collected with
correspondence to the risk factors with which the medical practitioner can
predict the disease before it occurs. The term mining means bringing out
patterns which are hidden and previously unidentified for better grasp of the
particular problem. Several data mining techniques such as the Classification
algorithms like Decision tree, Genetic algorithm, Neural network, Artificial
intelligence, Naive Bayes, and Clustering algorithms like Support Vector
Machine K-means and KNN are the techniques that are utilized in this process. Many
models for this prediction system are developed, comparisons are made and the
accuracy level of every model is provided.