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Abstract— The successful application of data mining in highly
visible fields like e-business, commerce and trade has led to its
application in other industries. The medical environment is still
information rich but knowledge weak. There is a wealth of data
possible within the medical systems. However, there is a lack of
powerful analysis tools to identify hidden relationships and
trends in data. Heart disease is a term that assigns to a large
number of heath care conditions related to heart. These medical
conditions describe the unexpected health conditions that directly
control the heart and all its parts. Medical data mining
techniques like association rule mining, classification, clustering
is implemented to analyze the different kinds of heart based
problems. Classification is an important problem in data mining.
Given a database contain collection of records, each with a single
class label, a classifier performs a brief and clear definition for
each class that can be used to classify successive records . A
number of popular classifiers construct decision trees to generate
class models. The data classification is based on MAFIA
algorithms which result in accuracy, the data is estimated using
entropy based cross validations and partition techniques and the
results are compared. C4.5 algorithm is used as the training
algorithm to show rank of heart attack with the decision tree.
The heart disease database is clustered using the K-means
clustering algorithm, which will remove the data applicable to
heart attack from the database.
Keywords—Data mining; MAFIA (Maximal Frequent Itemset
Algorithm); C4.5 Algorithm; K-means clustering
I. INTRODUCTION
Data mining is process of extracting hidden knowledge
from large volumes of raw data.Datamining is used to discover
knowledge out of data and presenting it in a form that is easily
understand to humans
Disease Prediction plays an important role in data mining.
Data Mining is used intensively in the field of medicine to
predict diseases such as heart disease, lung cancer, breast
cancer etc.
This paper analyzes the heart disease predictions using
different classification algorithms . Medicinal data mining has
high potential for exploring the unknown patterns in the data
sets of medical domain .These patterns can be used for medical
analysis in raw medical data.Heart disease was the major cause
of casualties in the world. Half of the deaths occur in the
countries like India, United States are due to cardiovascular
diseases. Medical data mining techniques like Association Rule
Mining, Clustering, Classification Algorithms such as Decision
tree,C4.5 Algorithm are implemented to analyze the different
kinds of heart based problems. C4.5 Algorithm and Clustering
Algorithm like K-Means are the data mining techniques used in
medical field [1]. With the help of this technique, the accuracy
of disease can be validated
II. RELATED WORKS
The difficult of recognizing constrained association rules for
heart illness prediction was studied by Carlos Ordonez. The
data mining techniques have been engaged by various works
to analyze various diseases, for instance: Hepatitis, Cancer,
Diabetes, Heart diseases. According to WHO (World Health
Organization), heart disease is the main cause of death in the
UK, USA, Canada, England [2]. Heart disease kills one in
every 32 seconds in USA .25.4% of all deaths in the USA
today are caused by heart disease. Jyothi Soni et.al [3]
proposed for predicting the heart disease using association rule
mining technique, they have generated a large number of rules
when association rules are applied to dataset .Frequent Itemset
Mining is used to find all frequent itemsets. Association rule
mining methods like Apriority and FPgrowth are used
frequently[4].Genetic algorithm have been used in [6], to
reduce the actual data size to get the optimal subset of
attributed sufficient for heart disease prediction. Classification
is one of the supervised learning methods to extract models
describing important classes of data. Three classifiers e.g.
Decision Tree, Naïve Bayes and Classification via clustering
have been used to diagnose the Presence of heart disease in
patients.
III. MAFIA
The association rule mining is a very important problem in
the data-mining field with numerous practical applications,
including consumer medical data analysis and network
intrusion detection .
Maximal Frequent Itemset Algorithm (MAFIA) is an
algorithm used for mining maximal frequent item sets from a
transactional database [7].It integrates a depth-first traversal
International Journal of Technical Research and Applications e-ISSN: 2320-8163,
ijtra.com Volume 1, Issue 5 (Nov-Dec 2013), PP. 41-45
42 | P a g e
of the itemset lattice with effective pruning mechanisms.
MAFIA efficiently stores the transactional database as a
series of vertical bitmaps. . If support(X)=minSup, we say
that X is a frequent itemset, and we denote the set of all
frequent itemsets by FI .The process for finding association
rules has two separate phases. In the first step, we find
the set of frequent itemsets (FI) in the database T. In the
second step, we use the set FI to generate “interesting”
patterns, and various forms of interestingness have been
proposed. In practice, the first step is the most timeconsuming.
Smaller alternatives to FI that still contain
enough information for the second phase have been
proposed including the set of frequent closed itemsets FCI.
Pseudocode:Simple(Current nodeC, MFI){
For each itemiinC.tail {
newNode=C U i
if newNode is frequent
Simple(newNode,MFI)}
if (Cis a leaf and C.head is not in MFI)
AddC.head to MFI
}
IV. C4.5 ALGORITHM
Decision trees are powerful and popular tools for
classification and prediction [5]. Decision trees produce rules,
which can be inferred by humans and used in knowledge
system such as database. C4.5 is an algorithm for building
decision trees .It is an extension of ID3 algorithm and it was
designed by Quinlan .It converts the trained trees (i.e. the
output of the ID3 algorithm) into sets of if-then rules. It
handles discrete and continuous attributes. C4.5 is one of
widely-used learning algorithms.
C4.5 algorithm builds decision trees from a set of
training data using the concept of information entropy.
C4.5 is also known as a statistical classifier.
- Check for base cases.
- For each element x, discover the normalized
information gain from dividing on x.
o Let x_best be the element with
the highest normalized
information gain.
- Create a decision node that breaks on a best.
- Repeats on the sublists obtained by dividing on
x_best, and add those nodes as children of node.
V. K-MEAN CLUSTERING
Clustering is a technique in data mining to find
interesting patterns in a given dataset .The k-means
algorithm is an evolutionary algorithm that gains its name
from its method of operation. The algorithm clusters
informations into k groups, where k is considered as an
input parameter. It then assigns each information’s to
clusters based upon the observation’s proximity to the mean
of the cluster. The cluster’s mean is then more computed
and the process begins again. The k-means algorithm is one
of the simplest clustering techniques and it is commonly
used in medical imaging and related fields. .K-Means
algorithm is a divisive, unordered method of defining
clusters. The phases convoluted in a k-means algorithm are
given consequently:
Prophecy of heart disease using K – Means clustering
techniques
o The algorithm arbitrarily selects k points as the initial
cluster centers (“means”).
o Each point in the dataset is assigned to the closed
cluster, based upon the Euclidean distance between
each point and each cluster center.
o Each cluster center is recomputed as the average of
the points in that cluster.
o Steps 2 and 3 repeat until the clusters converge.
Convergence may be explained differently depending
upon the performance, but it regularly explains that
either no observations change clusters when steps 2
and 3 are repeated or that the changes do not make a
material difference in the definition of the clusters.
The clustering is performed on preprocessed data set
using the K-means algorithm with the K values so as to
extract relevant data to heart attack. K-Means clustering
produces a definite number of separate, non-hierarchical
clusters. K-Means algorithm is a disruptive, non-hierarchical
method of defining clusters.
VI. SYSTEM ARCHITECTURE
Medical Dataset K-mean clustering
Cluster relevant data
Maximal Frequent
Itemset Algorithm
Select the Frequent
Pattern
Classify the pattern
using C4.5 algorithm
International Journal of Technical Research and Applications e-ISSN: 2320-8163,
ijtra.com Volume 1, Issue 5 (Nov-Dec 2013), PP. 41-45
43 | P a g e
TABLE I. HEART DISEASE DATASET
VII. EXPERIMENTAL RESULTS
The results of our experimental results in identifying
important patterns for predicting hear diseases are presented
n this section. The heart disease database is preprocessed
successfully by deleting corresponding records and
providing missing values as shown in table I. The well
mannered heart disease data set, resulting from
preprocessing, is then collected by K-means algorithm with
the K value of 2.One collection contains of the data related
to the heart disease as shown in table II and the further
contains the left over data. Then the regular forms are
mined efficiently from the collection suitable to heart
disease, using the MAFIA algorithm. The model
consortiums of heart attack parameters for general and
risk level along with their values and levels are listed
below In that, ID lesser than of (#1) of weight contains the
normal level of prediction and higher ID other than #1
comprise the higher risk levels and mention the
prescription IDs. Table III display the parameters o f
heart attack prediction w i t h equivalent prescription
ID and their levels. Table IV show the example of training
data to foresee the heart attack level and then figure 1
shows the efficient heart attack level with tree using the
C4.5 by information gain.