Journal of Software Engineering and Applications, 2012, 5, 682-686
http://dx.doi.org/10.4236/jsea.2012.59081 Published Online September 2012 (http://www.SciRP.org/journal/jsea)
Software Reuse in Cardiology Related Medical Database
Using K-Means Clustering Technique
M. Bhanu Sridhar1, Y. Srinivas2, M. H. M. Krishna Prasad3
1Department of Computer Science and Engineering, Raghu Engineering College, Visakhapatnam, India; 2Department of Information
Technology, GITAM University, Visakhapatnam, India; 3Department of Information Technology, Jawaharlal Nehru Technological
University, Kakinada, India.
Received May 1st, 2012; revised June 6th, 2012; accepted June 25th, 2012
ABSTRACT
Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software
reuse is a process in which the existing software is used to build new software. A metric is a quantitative indicator of an
attribute of an item/thing. Reusability is the likelihood for a segment of source code that can be used again to add new
functionalities with slight or no modification. A lot of research has been projected using reusability in reducing code,
domain, requirements, design etc., but very little work is reported using software reuse in medical domain. An attempt
is made to bridge the gap in this direction, using the concepts of clustering and classifying the data based on the dis-
tance measures. In this paper cardiologic database is considered for study. The developed model will be useful for Doc-
tors or Para-medics to find out the patient’s level in the cardiologic disease, deduce the medicines required in seconds
and propose them to the patient. In order to measure the reusability K-means clustering algorithm is used.
Keywords: Reuse; Cardiology; Software Metrics; Clustering, K-Means; Cardiac
1. Introduction
Software Reuse is currently one of the most active and
creative research areas in Software Engineering. It offers
a solution to reduce repeated work and improve effi-
ciency and quality in software development and man-
agement. It makes use of the experience obtained in the
past development process. In the proposed article we
have considered the database of the heart patients from [1]
to focus on the cardiologic situations. Reuse is vital in
medical field because the previous information is very
handy in deducing a patient’s current health position and
save the precious life [2].
Cardiology is a medical specialty dealing with human
heart disorders. This field includes diagnosis and treat-
ment of disorders like heart defects, heart failure and
other heart diseases. According to World Health Organi-
zation, India has the highest number of coronary heart
disease deaths in the world [3]. This can be deduced not
only due to lack of resources but also due to concentra-
tion of resources at places like cities and towns. By usage
of Internet and cardiology database component reuse, the
Para-medics, can deduce the medicines or methods to be
used for the patients at remote places to temporarily put
them out of danger. From the reuse of available data, the
required medicines may also be deduced and proposed to
the patients.
In this article we propose a methodology using the
clustering technique together with classification tech-
nique where the heart patients’ data is clustered, depend-
ing on the health conditions, into three categories: normal,
pro-cardiac and cardiac. We use the Euclidean distance
measure to classify the patients’ disease level conditions
into the three specified categories. The paper is organized
as follows: Section 1 of the paper deals with introduction;
in Section 2, categories of the heart patients is presented;
K-means algorithm is presented in Section 3; Section 4
deals with the methodologies together with experimental
results and finally the conclusion is presented in Section
5.
Our future work, which is at a research stage now
would be very useful in aiding to the ailing patients and
become an important part in the general usage of the
Doctors.
2. Categories of Heart Patients
The heart is a myogenic (cell-related) muscular organ
with a circulatory system (including all vertebrates), that
is responsible for pumping blood throughout the blood
vessels by repeated, rhythmic contradictions [4]. Among
the problems related to heart, the major problem is car-

Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique 683
diac arrest, which is the cessation of normal blood circu-
lation due to failure of the heart to contract effectively. It
should be effectively realized that cardiac arrest is dif-
ferent from a heart attack where blood supply is inter-
rupted to a part of the heart which may/may not lead to
the patient’s death.
The patients who approach a doctor can be classified
into three categories taking into consideration results of
different tests conducted with the existing symptoms.
The properties taken into consideration are Atherosclero-
sis (due to Cholesterol), Myocardial Infarction (heart
attack), different medical signs like blood cell count and
skin rashness, various symptoms like head ache and body
pain, and other facts like Diabetes, Triglyceride, Mi-
graine and so on [5].
Armed with all this information, the concerned patient
is placed in one of the quoted three categories below.
2.1. Normal
A patient can be declared “normal” when no signs or
symptoms of a cardiovascular/coronary disease are found
within the results of various tests conducted. The general
factors considered are the blood pressure (BP), sugar
level in blood, results of Electrocardiography (ECG),
Cholesterol level, Triglyceride, and other sensations. A
normal patient should have the BP within control
(<120/80 mmHg), Blood sugar level on waking up with
an empty stomach between 80 to 120 mg/dl [6], normal
output from the cardiac stress test conducted with the
ECG [7], and no other notable problems. It should also
be noted that a now normal patient might suffer from a
heart stroke soon or later since he had inherited the prob-
lem, of which the reports wouldn’t mention.
2.2. Pro-Cardiac
Pro-cardiac category keeps the account of those patients
who are suspected to have some signs and/or symptoms
of heart-problems. These can be observed from the BP
tests slightly exceeding the normal levels, sugar levels in
blood also rising, ECG suspecting (though not deducing)
problems in future and some signs and symptoms like
light chest pain, high cholesterol, severe head aches often
turning up etc. do surface.
A pro-cardiac becomes a suspect of cardiac problems
in near future and is advised by the Doctor not only to
take a bit of medicines but also to consider doing regular
exercises like light running, and other methods to bring
his yet-controllable level to the normal state.
2.3. Cardiac
As it might be suspected, a cardiac is surely in the prob-
lematic range: prone to abnormal BP conditions, having
severe pain the chest region, burning sensations, sweat-
ing, pain along the left arm and finally having already
had a light heart attack. A cardiac must be immediately
taken into consideration for regular treatment with con-
stant observation of all concerned positions in and
around the heart and those that affect the heart. A cardiac
is also advised to taken high-power medicines and con-
duct long walks every day so as to keep the blood-
pumping in the heart at a normal position.
After mentioning and discussing all the classification
parts, it should also be noted that effective medical data
of the patient should be readily available for the Doctors
which also should be frequently updated. This data forms
the backbone of the patient’s classification level, severity
level and the chance of saving his/her life. An attempt is
made in this paper, by bringing into picture the reuse of
data, to correctly judge the patient’s position.
3. K-Means Clustering Algorithm
Clustering in data mining is the process of grouping a set
of objects into classes of similar objects [8]. Many clus-
tering algorithms are discussed in the literature and the
most important of these are partitioning and hierarchical
algorithms. K-means remains one of the most popular
clustering algorithms used in practice [9]. The main rea-
sons are it is simple to implement, fairly efficient, results
are easy to interpret and it can work under a variety of
conditions. The steps to be followed for effective clus-
tering using K-means algorithm are:
Step 1. Begin with a decision on the value of K =
number of segments
Step 2. Put any initial partition that classifies the data
into K segments. We can arrange the training samples
randomly, or systematically as follows:
1) Take the first K training samples as a single-element
Segment.
2) Assign each of the remaining (N-K) training sam-
ples to the segment with the nearest centroid. Let there be
exactly K segments (C1, C2 - CK) and n patterns to be
classified such that, each pattern is classified into exactly
one segment. After each assignment, re-compute the cen-
troid of the gaining segment.
Step 3. Take each sample in sequence and compute its
distance from the centroid of each of the segments. If the
sample is not currently in the cluster with the closest
centroid switch this sample to that segment and update
the centroid of the segment gaining the new sample and
cluster losing the sample.
Step 4. Repeat step 3 until convergence is achieved,
that is until a pass through the training sample causes no
new assignments. After determining the final value of the
K (number of regions) we obtain the estimates the pa-
rameters μi, σi and αi for the ith region using the seg-
mented regions.

Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique 684
4. Methodology and Experimental Results
In this article a novel methodology for cardiac medical
data reusability is proposed. A database from archives
[11] is considered for carrying out our proposed work. In
this method, we have first categorised the data into 3
groups namely, normal, pro-cardiac and cardiac. We
have considered the scenario of Chintapalli, a remote
tribal village in Andhra Pradesh, India, where no super-
speciality services for treating cardiac patients are avail-
able. It is necessary in such conditions to supplement the
patient with sufficient primary aid so that he can sustain
for the minimum period of shifting. Depending upon the
clinical reports of the patient’s data, he is to be catego-
rized into one of the levels presented in Sections 2.1-2.3.
A dissimilarity matrix is constructed with the readings
from the clinical observations and identifying the most
leading factors that may be prone to the cardiac diseases
as per the experts’ references. The various readings con-
sidered are categorized into the above mentioned three
groups and a database is formulated from the realistic
data obtained from medical patients from the data re-
ferred in [10]. The predominant features considered in
the database are: blood pressure (BP), heartbeat (HB),
pulse rate (PR), ECG (normal/abnormal), pain in the left
shoulder region, sweating, nausea/vomiting, over weight,
chest pain and breathlessness.
For the testing purpose in this paper, we have used a
database of ten patients with the above mentioned ten
features; if the reading is present we have represented it
by using a value 1 else 0 (binary). Following this proce-
dure for the other inputs, a binary matrix [11] is obtained
and this matrix is to be categorized; K-Means algorithm
is utilized for the same. Now within the clusters, the ho-
mogenous data is obtained. To classify a patient, the dis-
similarity matrix is again formulated and is classified by
calculating the minimum distance between the posed
query data and the retrieved data by using the clustering
technique.
Reuse Metrics
The reuse components for partitioning the data are di-
vided into 4 steps performed at each phase in preparation
to the next phase. These steps are:
1) Developing a reuse plan or strategy after studying
the problem and available solutions to the problem.
2) Identifying a solution structure for the problem fol-
lowing the reuse plan or strategy.
3) Reconfiguring the solution structure to improve the
possibility of using predefined components available at
the next phase.
4) Evaluating the system.
The major tasks under the first step are to understand
the problem about the cardiac patients, build-up the
knowledge for categorizing them into groups and de-
velop a plan or strategy for their treatment. In the second
step, apply the knowledge to develop a solution structure
that is best suited for the problem following the reuse
plan or strategy developed in the above phase. In the next
step, reconfigure the solution in order to optimize the
reuse both at both the current phase and next phase. Fi-
nally the computed components are to be classified using
test features.
The data of 10 patients, from the archives [10] is con-
verted into a binary matrix as above. The concepts in the
clustering partition in reusable components [8] are util-
ized to construct a Java program that takes in the data
from the Table 1. The program constructs the clusters by
classifying the data using the Euclidean distance. After
the K-Means clustering, the data is divided based on the
binary clustering, into three groups. The patients with Ids
(P4, P7, P3, P9, P10) belong to the first cluster, patients
with Ids (P8, P2, P1) belong to the second cluster and
patients with Ids (P5, P6, P10) belong to the third cluster.
The basic aim in this context is to assist the patients
with minimum first aid for sustainability till he/she is
shifted to the nearest multi-speciality clinic from the re-
mote place Chintapalli considered here. In order to cate-
gorize the patients, it is necessary to identify the exact-
ness of the category and thereby suggesting the minimum
essential supportive drugs to maintain or better the cur-
rent condition. It becomes clear by now that it is neces-
sary to find the exactness of the disease if we are to
achieve our goals.
To find the most exact solution in this concept, an
auto-correlation model is used to find the exact correla-
tion and categorization of the patients. The auto-correla-
tion formula used here is given by


1
2
1
n
ttk
tk
kn
t
t
YYY Y
r
YY








where t is the patient with the first symptom, K + 1 is the
patient with the second symptom and so on.
In this model, we try to correlate the data to each pa-
tient by considering the auto-correlation model and the
results obtained are tabulated (Figure 1).
From the above considered data, it can be clearly seen
that the patient with R6 is having highest auto-correlation
factor and is likely to have symptoms of a cardiac. The
value obtained here is 0.9. The patient with Ids P5 and P6
i.e. R5 and R6 have the next immediate ranges and they
are also likely to be cardiac-prone. The values obtained
by using the above quoted autocorrelation formula are
given under:
R1 = 0.3, R2 = 0.3, R3 = 0.1, R4 = 0.0023, R5 = 0.7,
6 = 0.9, R7 = 0.11, R8 = 0.3, R9 = 0.1, R10 = 0.72 R

Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique 685
Figure 1. The results obtained from Autocorelation.
Table 1. The symptoms (→) of the patients.
Patient ID
(↓) BP Heart beat (HB)
Pulse rate
(PR) ECG Left shoulder painSweatingVomitingOver weight Chest pain Breathlessness
P1 1 0 1 0 1 1 0 0 0 0
P2 0 0 1 1 1 1 0 0 0 0
P3 0 0 1 0 0 0 0 1 0 0
P4 0 0 0 0 0 0 0 0 0 0
P5 0 1 1 1 1 1 1 1 1 0
P6 1 1 1 1 1 1 1 1 1 1
P7 0 0 0 0 0 0 0 0 1 0
P8 0 0 1 1 1 0 0 0 0 0
P9 0 0 0 0 1 0 0 0 1 0
P10 0 1 0 1 0 1 0 1 0 1
Here R6 is maximum, which specifies that the person
is more likely to belong to the category cardiac; R1, R3,
R4, R7, R8, R9 are at minimum risk and they belong to
normal case and R2, R5 belong to the category pro-car-
diac.
We have also tried to estimate the significance of each
symptom for each patient over the other symptoms using
auto-correlation and could identify the symptom that
would be leading to cardiac problems.
We now input a new patient’s data to check out the
cluster where it belongs to; the Java program promptly
supplies us the answer. The output of the Java program is
given in Figure 2.
From the screenshot Figure 2, it can be easily identi-
fied that the given test data belongs to a particular cluster.
Utilizing the classification given in Section 2, we obtain
the concerned category.
5. Conclusions
In this paper a new methodology for software reuse in
cardiac domain is presented. A database is considered or
generated with 10 patients and is categorised into 3
categories depending upon the health conditions. The
readings for these categories are obtained from the super
speciality doctors, and are used for checking the reus-
Figure 2. The results of classification.

Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique 686
ability. The dissimilarity matrix is generated and the
clustering is performed on the binary data. Classification
is carried out on the test data by finding the minimum
distance using Euclidean distance, and the reusability for
partitioning is carried out as prescribed by Boris Deli-
basic et al. [8] are presented in Section 4.1.
The results obtained from the K-Means algorithm are
given as inputs to the auto-correlation model to catego-
rize the patients more accurately to be declared a car-
diac.The model developed will be immensely useful for
the Doctors to prescribe the medicines used for the pre-
vious patients of the respective cluster to the new patient
immediately without spending time in checking condi-
tions. It may be much more valuable for the Para-medics
at remote places who can save the life of the patient.
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