Prediction of Heart Disease in Diabetic patients using Naive Bayes Classification Technique

International Journal of Computer Applications Technology and Research
Volume 7–Issue 07, 255-258, 2018, ISSN:-2319–8656
www.ijcat.com 255
Prediction of Heart Disease in Diabetic patients using
Naive Bayes Classification Technique
Charu V.Verma
Research scholar (CSE)
Dr. C.V. Raman University
Bilaspur, India
Dr. S. M. Ghosh
Professor
Dr. C.V. Raman University
Bilaspur, India
Abstract: The objective of our paper is to predict the risk of heart disease in diabetic patients. In this research paper we are applying
Naive Bayes data mining classification technique which is a probabilistic classifier based on Bayes theorem with strong (naive)
independence assumptions between the features. Data mining techniques have been widely used in health care systems for prediction
of various diseases with accuracy. Health care industry contains large amount of data and hidden information. Effective decisions are
made with this hidden information by applying data mining techniques. These techniques are used to discover hidden patterns and
relationships from the datasets. The major challenge facing the healthcare industry is the provision for quality services at affordable
costs. A quality service implies diagnosing patients correctly and treating them effectively. In this proposed system certain attributes
are consider in diabetic patients to predict the risk of heart disease
Keywords: Heart Disease, Diabetes, Data Mining, KDD, Naïve Bayes.
1. INTRODUCTION
The development of Information Technology has generated
large amount of databases and huge data in various areas. The
area includes health sector, financial sector, weather
forecasting, education, manufacturing, fraud detection, bio
information etc. The research in databases and information
technology has given rise to an approach to store and
manipulate this useful data for further decision making. Data
mining is the process of discovering patterns in large data
bases involving methods at the intersection of machine
learning, statistics, and database systems [1]. Popularly data
mining referred as knowledge discovery from the data. It is
the automated or convenient extraction of patterns
representing knowledge implicitly stored or captured in large
databases, data warehouses, the Web, other massive
information repositories or data streams. The knowledge
discovery is an interactive process, consisting by developing
an understanding of the application domain, selecting and
creating a data set, preprocessing, data transformation.
Figure 1: KDD knowledge discovery process
Data mining is the exploration of large datasets to extract
hidden and previously unknown patterns, relationships and
knowledge that are difficult to detect with traditional
statistical methods. Data mining plays a vital role in field of
health sector since here huge amounts of data is generated
which are too complex and voluminous to be processed and
analyzed by traditional methods and difficult to handle
manually. Data mining can help healthcare insurers detect
fraud and abuse, healthcare organizations make customer
relationship management decisions, physicians identify
effective treatments and best practices, and patients receive
better and more affordable healthcare services. In our busy
schedule, most of the people work like a machine in order to
live a deluxe and comfortable life in future and to earn more
money, but during this type of situation people forget about
their health and even don’t take any proper rest. Because of
this they affected from various type of diseases at a very early
age, which cause our health as Diabetes, Heart Disease,
Cancer, Eyes, Kidney failure and many more.
Diabetes Mellitus (DM) is commonly referred as Diabetes; it
is the condition in which the body does not properly process
food for use as energy. Most of the food we eat is turned into
glucose or sugar for energy. The pancreas, an organ makes a
hormone called insulin to help glucose get into the cells of our
bodies. When a body is affected with diabetes, it couldn’t
make enough insulin or couldn’t use its own insulin. This
causes sugar to build up into blood. Several pathogenic
processes are involved in the development of diabetes. These
range from autoimmune destruction of the β-cells of the
pancreas with consequent insulin deficiency to abnormalities
that result in resistance to insulin action. Diabetes is a life
threatening disease in rural and urban, then developed and
under developed countries. The common symptoms for the
diabetic patients are frequent urination, increased thirst,
weight loss, slow-healing in wound, giddiness, increased
hunger etc. Diabetes can cause serious health complications
including heart disease, blindness, kidney failure and low-
extremity amputations.
Types of Diabetes
Type 1Diabetes is called insulin-dependent diabetes mellitus
(IDDM) or juvenile-onset diabetes. Autoimmune, genetic, and
environmental factors are involved in the development of this
type of diabetes. Type1 mostly occurs in young people who

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are below 30 years. This type can affect children or adults, but
majority of these diabetes cases were in children. In persons
with type 1 diabetes, the beta cells of the pancreas, which are
responsible for insulin production, are destroyed due to
autoimmune system.
Type 2 Diabetes is called non-insulin-dependent diabetes
mellitus (NIDDM) or adult-onset diabetes. In the type 2
diabetes, the pancreas usually produces some insulin the
amount produced is not enough for the body's needs, or the
body's cells are resistant to it. Risk factors for Type 2 diabetes
includes older age, obesity, family history of diabetes, prior
history of gestational diabetes, impaired glucose tolerance,
physical inactivity, and race/ethnicity.
Gestational Diabetes is the third main form and occurs when
pregnant women without a previous history of diabetes
develop a high blood glucose level. The majority of
gestational diabetes patients can control their diabetes with
exercise and diet. In such cases between 10%-20% of them
will need to take some kind of blood-glucose-controlling
medications. In few cases this gestational diabetes may lead to
type 2 diabetes in future. It affects on 4% of all pregnant
women [2].
Heart disease is the leading cause of death in the world over
the past 10 years. Researchers have been using several data
mining techniques in the diagnosis of heart disease. Heart
diseases are the number 1 cause of death globally: more
people die annually from heart disease than from any other
cause. An estimated 17.7 million people died from heart
disease in 2015, representing 31% of all global deaths. Of
these deaths, an estimated 7.4 million were due to coronary
heart disease and 6.7 million were due to stroke. Over three
quarters of heart disease deaths take place in low- and middle-
income countries. Out of the 17 million premature deaths
(under the age of 70) due to non communicable diseases in
2015, 82% are in low- and middle-income countries, and 37%
are caused by this disease. People with cardiovascular disease
or who are at high cardiovascular risk (due to the presence of
one or more risk factors such as hypertension, diabetes,
hyperlipidaemia or already established disease) need early
detection and management using counseling and medicines, as
appropriate [3]. Now a day’s heart disease is a major health
problem and cause of death all over the world. Heart is a very
valuable part of our body, and plays a very important role in
our life. Our whole life depends on efficient working of heart.
The most important behavioral risk factors of heart disease
and stroke are unhealthy diet, physical inactivity, tobacco use
and harmful use of alcohol. The effects of behavioral risk
factors may show up in individuals as raised blood pressure,
raised blood glucose, raised blood lipids, and overweight and
obesity. These “intermediate risks factors” can be measured in
primary care facilities and indicate an increased risk of
developing a heart attack, stroke, heart failure and other
complications.
Heart disease is caused due to narrowing or blockage of
coronary arteries. This is caused by deposition of fat on inner
walls of arteries and also due to build up cholesterol. There
are some of major heart disease factors which include
Diabetes, high blood pressure, high cholesterol, obesity,
family history, smoking, eating habits, alcohol that affects our
whole body.
2. METHODOLOGY APPLIED
DATA MINING in health care has become increasingly
popular because it can improved our patient care by early
detecting of disease supports helping care providers for
treatment programs and reduces the cost of health care. [4]
Data Mining is major anxious with the study of data and Data
Mining tools and techniques are used for discovery patterns
from the data set. The most important aim of Data Mining is
to find patterns mechanically with least user input and efforts.
Data Mining is an influential tool able of usage decision
building and for forecasting expectations trends of market.
Data Mining tools and techniques can be effectively
functional in different fields in different forms. Many
Organizations now begin using Data Mining as a tool, to
contract with the aggressive surroundings for data analysis.
By using Mining tools and techniques, different fields of
business get advantage by simply assess various trends and
pattern of market and to make rapid and efficient market trend
analysis. Data mining is very helpful tool for the diagnosis of
diseases.
Classification Technique
Classification derives a model to determine the class of an
object based on its attributes. A collection of records will be
available, each record with a set of attributes. One of the
attributes will be class attribute and the goal of classification
task is assigning a class attribute to new set of records as
accurately as possible. Mainly classification is used to classify
every item in a set of data into one of predefined set of classes
or groups.
Naïve bayes is one of technique of classification used for
prediction of data. Naive Bayes classifiers is a probabilistic
classifiers based on applying Bayes' theorem with strong
(naive) independence assumptions between the predictors. A
Naive Bayesian model is easy to build, with no complicated
iterative parameter estimation which makes it particularly
useful for large datasets such as in the field of medical science
for diagnosing heart patients. Despite its simplicity, the Naive
Bayesian classifier often does surprisingly well and is widely
used because it often outperforms more sophisticated
classification methods [8]. Bayes theorem provides a way of
calculating the posterior probability, P(c|x), from P(c), P(x),
and P(x|c). Naive Bayes classifier assumes that the effect of
the value of a predictor (x) on a given class (c) is independent
of the values of other predictors. This assumption is called
class conditional independence.
2.1 Equations:
• P(c|x) is the posterior probability of class (target) given
predictor (attribute).
• P(c) is the prior probability of class.
• P(x|c) is the likelihood which is the probability of predictor
given class.

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• P(x) is the prior probability of predictor Where C and X are
two events. Where C and X are two events. Such Naive Bayes
model is easy to build and particularly useful for very large
data sets. Along with simplicity, Naive Bayes is known
to outperform even highly sophisticated classification
methods [4].
2.2 Processing Data set
The patient data set is compiled from UCI data repositories as
combined data from Statlog data set and Cleveland Clinic
Foundation for heart patients. Here we are taken 14 attributes
with nominal values from the database that are considered for
the required prediction they are age, sex, chest pain(cp),
Blood Pressure(trestbsp), diabetes(fbs)(its value is always 1),
ECG(restecg), Heart Rate(thalach), exang, oldpeak, slope,
thal, blood Cholesterol(chol) and num (heart disease
diagnosis). Where if the num value is present then there is
presence of heart disease and if the num value is absent then
no heart disease.
2.3 Tool used
Waikato Environment for Knowledge Analysis (WEKA) has
been used for prediction due to its proficiency in discovering,
analysis and predicting patterns. It was developed at the
University of Waikato in New Zealand and easiest way to use
is through a graphical user interface called Explorer. Weka is
a collection of machine learning algorithms for data mining
tasks, written in Java and contains tools for data pre-
processing, classification, regression, clustering, association
rules, and visualization
2.4 10 fold cross validation
Cross-validation, a standard evaluation technique, is a
systematic way of running repeated percentage splits. Cross-
validation is a technique to evaluate predictive models by
partitioning the original sample into a training set to train the
model, and a test set to evaluate it. In k-fold cross-validation,
the original sample is randomly partitioned into k equal size
subsamples. Of the k subsamples, a single subsample is
retained as the validation data for testing the model, and the
remaining k-1 subsamples are used as training data. The
cross-validation process is then repeated k times (the folds),
with each of the k subsamples used exactly once as the
validation data. The k results from the folds can then be
averaged (or otherwise combined) to produce a single
estimation. The advantage of this method is that all
observations are used for both training and validation, and
each observation is used for validation exactly once[7].
Divide a dataset into 10 pieces (“folds”), then hold out each
piece in turn for testing and train on the remaining 9 together.
This gives 10 evaluation results, which are averaged. In
“stratified” cross-validation, when doing the initial division
we ensure that each fold contains approximately the correct
proportion of the class values. Having done 10-fold cross-
validation and computed the evaluation results, Weka invokes
the learning algorithm a final (11th) time on the entire dataset
to obtain the model that it prints out [6].
We will simply define and calculate the accuracy, sensitivity,
and specificity from the confusion matrix.
True positive (TP) = the number of cases correctly identified
as patient
False positive (FP) = the number of cases incorrectly
identified as patient
True negative (TN) = the number of cases correctly identified
as healthy
False negative (FN) = the number of cases incorrectly
identified as healthy
Accuracy: The accuracy of a test is its ability to differentiate
the patient and healthy cases correctly. To estimate the
accuracy of a test, we should calculate the proportion of true
positive and true negative in all evaluated cases.
Mathematically, this can be stated as:
Accuracy= (TP+TN)/TP+TN+FP+FN
Sensitivity: The sensitivity of a test is its ability to determine
the patient cases correctly. To estimate it, we should calculate
the proportion of true positive in patient cases.
Sensitivity=TP/TP+FN
Specificity: The specificity of a test is its ability to determine
the healthy cases correctly. To estimate it, we should calculate
the proportion of true negative in healthy cases.
Specificity=TN/TN+FP
3. EXPERIMENTAL RESULT
The result in our experiment is given of heart disease in
diabetic patients. In our experiment we had proposed Naive
bayes classification technique as experimental result shown in
the figure 2. Accuracy, Sensitivity, Specificity has been
calculated from the both confusion matrix of both the
performed experiments. For heart disease model accuracy is
89.41%, Sensitivity is 46.05%, Specificity is 55.55%
Figure 2 Result window of heart disease data set having
Diabetes
Table 1. Result of detailed accuracy class of diabetes
patient having heart disease
Tp Fp Pre Recall Fm ROC

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Heart
disease
with
diabetes
0.894 0.105 0.895 0.894 0.894 0.904
4. CONCLUSION
In this research paper application of data mining is used to
analyze the clinical dataset to detect diseases and diagnosis
based on the data and the attributes provided. In the proposed
works Naive Bayes classification technique helps to predict
heart disease in diabetic patient. From the system we get
confusion matrix from which we can predict accuracy of the
applied Naïve Bayes algorithm. The result shows that
accuracy of our applied algorithm is 89.41% in the prediction
of risk of heart disease in diabetic patient. As a future work,
further data analysis has been planned to perform other data
mining algorithms to improve the classification accuracy.
5. REFERENCES
[1] https://en.wikipedia.org/wiki/Data_mining
[2] http://www.who.int/news-room/fact-
sheets/detail/cardiovascular-diseases-(cvds)
[3] https://www.researchgate.net/publication/312188365
”Heart Attack prediction using Data mining technique”
[4] https://www.analyticsvidhya.com/blog/2017/09/naive-
bayes-explained/
[5] https://www.futurelearn.com/courses/data-mining-with-
weka/0/steps/25384.
[6] https://www.openml.org/a/estimation-procedures/1
[7] http://www.saedsayad.com/naive_bayesian.htm
[8] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4614595

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Prediction of Heart Disease in Diabetic patients using Naive Bayes Classification Technique